DISC InfoSec blog

Aug 26 2025

From Compliance to Trust: Rethinking Security in 2025

Category: AI,Information Privacy,ISO 42001 — disc7 @ 8:45 am

Cybersecurity is no longer confined to the IT department — it has become a fundamental issue of business survival. The past year has shown that security failures don’t just disrupt operations; they directly impact reputation, financial stability, and customer trust. Organizations that continue to treat it as a back-office function risk being left exposed.

Over the last twelve months, we’ve seen high-profile companies fined millions of dollars for data breaches. These penalties demonstrate that regulators and customers alike are holding businesses accountable for their ability to protect sensitive information. The cost of non-compliance now goes far beyond the technical cleanup — it threatens long-term credibility.

Another worrying trend has been the exploitation of supply chain partners. Attackers increasingly target smaller vendors with weaker defenses to gain access to larger organizations. This highlights that cybersecurity is no longer contained within one company’s walls; it is interconnected, making vendor oversight and third-party risk management critical.

Adding to the challenge is the rapid adoption of artificial intelligence. While AI brings efficiency and innovation, it also introduces untested and often misunderstood risks. From data poisoning to model manipulation, organizations are entering unfamiliar territory, and traditional controls don’t always apply.

Despite these evolving threats, many businesses continue to frame the wrong question: “Do we need certification?” While certification has its value, it misses the bigger picture. The right question is: “How do we protect our data, our clients, and our reputation — and demonstrate that commitment clearly?” This shift in perspective is essential to building a sustainable security culture.

This is where frameworks such as ISO 27001, ISO 27701, and ISO 42001 play a vital role. They are not merely compliance checklists; they provide structured, internationally recognized approaches for managing security, privacy, and AI governance. Implemented correctly, these frameworks become powerful tools to build customer trust and show measurable accountability.

Every organization faces its own barriers in advancing security and compliance. For some, it’s budget constraints; for others, it’s lack of leadership buy-in or a shortage of skilled professionals. Recognizing and addressing these obstacles early is key to moving forward. Without tackling them, even the best frameworks will sit unused, failing to provide real protection.

My advice: Stop viewing cybersecurity as a cost center or certification exercise. Instead, approach it as a business enabler — one that safeguards reputation, strengthens client relationships, and opens doors to new opportunities. Begin by identifying your organization’s greatest barrier, then create a roadmap that aligns frameworks with business goals. When leadership sees cybersecurity as an investment in trust, adoption becomes much easier and far more impactful.

How to Leverage Generative AI for ISO 27001 Implementation

ISO27k Chat bot

If the GenAI chatbot doesn’t provide the answer you’re looking for, what would you expect it to do next?

If you don’t receive a satisfactory answer, please don’t hesitate to reach out to us — we’ll use your feedback to help retrain and improve the bot.

The Strategic Synergy: ISO 27001 and ISO 42001 – A New Era in Governance

ISO 27001’s Outdated SoA Rule: Time to Move On

ISO 27001 Compliance: Reduce Risks and Drive Business Value

ISO 27001:2022 Risk Management Steps

How to Continuously Enhance Your ISO 27001 ISMS (Clause 10 Explained)

Continual improvement doesn’t necessarily entail significant expenses. Many enhancements can be achieved through regular internal audits, management reviews, and staff engagement. By fostering a culture of continuous improvement, organizations can maintain an ISMS that effectively addresses current and emerging information security risks, ensuring resilience and compliance with ISO 27001 standards.

ISO 27001 Compliance and Certification

ISMS and ISO 27k training

Security Risk Assessment and ISO 27001 Gap Assessment

At DISC InfoSec, we streamline the entire process—guiding you confidently through complex frameworks such as ISO 27001, and SOC 2.

Here’s how we help:

Conduct gap assessments to identify compliance challenges and control maturity
Deliver straightforward, practical steps for remediation with assigned responsibility
Ensure ongoing guidance to support continued compliance with standard
Confirm your security posture through risk assessments and penetration testing

Let’s set up a quick call to explore how we can make your cybersecurity compliance process easier.

ISO 27001 certification validates that your ISMS meets recognized security standards and builds trust with customers by demonstrating a strong commitment to protecting information.

Feel free to get in touch if you have any questions about the ISO 27001, ISO 42001, ISO 27701 Internal audit or certification process.

Successfully completing your ISO 27001 audit confirms that your Information Security Management System (ISMS) meets the required standards and assures your customers of your commitment to security.

Get in touch with us to begin your ISO 27001 audit today.

ISO 27001:2022 Annex A Controls Explained

Preparing for an ISO Audit: Essential Tips and Best Practices for a Successful Outcome

Is a Risk Assessment required to justify the inclusion of Annex A controls in the Statement of Applicability?

Many companies perceive ISO 27001 as just another compliance expense?

ISO 27001: Guide & key Ingredients for Certification

DISC InfoSec Previous posts on ISO27k

ISO certification training courses.

ISMS and ISO 27k training

DISC InfoSec previous posts on AI category

Tags: iso 27001, ISO 27701, ISO 42001

Comments (2)

Aug 25 2025

Understand how the ISO/IEC 42001 standard and the NIST framework will help a business ensure the responsible development and use of AI

Category: AI,ISO 42001,NIST CSF — disc7 @ 10:11 pm

The ISO/IEC 42001 standard and the NIST AI Risk Management Framework (AI RMF) are two cornerstone tools for businesses aiming to ensure the responsible development and use of AI. While they differ in structure and origin, they complement each other beautifully. Here’s a breakdown of how each contributes—and how they align.

🧭 ISO/IEC 42001: AI Management System Standard

Purpose:
Establishes a formal AI Management System (AIMS) across the organization, similar to ISO 27001 for information security.

🔧 Key Components

Leadership & Governance: Requires executive commitment and clear accountability for AI risks.
Policy & Planning: Organizations must define AI objectives, ethical principles, and risk tolerance.
Operational Controls: Covers data governance, model lifecycle management, and supplier oversight.
Monitoring & Improvement: Includes performance evaluation, impact assessments, and continuous improvement loops.

✅ Benefits

Embeds responsibility and accountability into every phase of AI development.
Supports legal compliance with regulations like the EU AI Act and GDPR.
Enables certification, signaling trustworthiness to clients and regulators.

🧠 NIST AI Risk Management Framework (AI RMF)

Purpose:
Provides a flexible, voluntary framework for identifying, assessing, and managing AI risks.

🧩 Core Functions

Function	Description
Govern	Establish organizational policies and accountability for AI risks
Map	Understand the context, purpose, and stakeholders of AI systems
Measure	Evaluate risks, including bias, robustness, and explainability
Manage	Implement controls and monitor performance over time

✅ Benefits

Promotes trustworthy AI through transparency, fairness, and safety.
Helps organizations operationalize ethical principles without requiring certification.
Adaptable across industries and AI maturity levels.

🔗 How They Work Together

ISO/IEC 42001	NIST AI RMF
Formal, certifiable management system	Flexible, voluntary risk management framework
Focus on organizational governance	Focus on system-level risk controls
PDCA cycle for continuous improvement	Iterative risk assessment and mitigation
Strong alignment with EU AI Act compliance	Strong alignment with U.S. Executive Order on AI

Together, they offer a dual lens:

ISO 42001 ensures enterprise-wide governance and accountability.
NIST AI RMF ensures system-level risk awareness and mitigation.

visual comparison chart or a mind map to show how these frameworks align with the EU AI Act or sector-specific obligations.

mind map comparing ISO/IEC 42001 and the NIST AI RMF for responsible AI development and use:

This visual lays out the complementary roles of each framework:

ISO/IEC 42001 focuses on building an enterprise-wide AI management system with governance, accountability, and operational controls.
NIST AI RMF zeroes in on system-level risk identification, assessment, and mitigation.

AIMS and Data Governance

Navigating the NIST AI Risk Management Framework: A Comprehensive Guide with Practical Application

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Managing Artificial Intelligence Threats with ISO 27001

DISC InfoSec previous posts on AI category

Tags: responsible development and use of AI

Comments (1)

Aug 25 2025

Analyze the impact of the AI Act on different stakeholders: autonomous driving

Category: AI — disc7 @ 3:26 pm

The EU AI Act introduces a layered regulatory framework that significantly affects stakeholders in the autonomous driving ecosystem. Because autonomous vehicles (AVs) rely heavily on high-risk AI systems—such as perception, decision-making, and navigation—their regulation is both sector-specific and cross-cutting. Here’s a structured analysis tailored to your compliance-oriented lens:

🚗 Autonomous Driving: Stakeholder Impact Analysis

1. Automotive Manufacturers

Obligations:
- Must ensure AI systems embedded in AVs meet high-risk requirements under the AI Act.
- Required to conduct conformity assessments and maintain technical documentation.
- Must align with both the AI Act and sectoral legislation like the Type-Approval Framework Regulation (EU 2018/858).
Risks:
- High compliance costs and technical complexity, especially for explainability and real-time monitoring.
- Exposure to fines up to €35 million or 7% of global turnover for non-compliance.
Opportunities:
- Regulatory alignment can enhance consumer trust and market access.
- Participation in AI regulatory sandboxes may accelerate innovation.

2. AI System Developers (Perception, Planning, Control Modules)

Obligations:
- Must classify systems by risk level and ensure robustness, safety, and transparency.
- Required to implement post-market monitoring and incident reporting.
Risks:
- Difficulty in making complex models explainable (e.g., deep neural networks for object detection).
- Liability for system failures or biased decision-making.
Opportunities:
- Demand for modular, certifiable AI components.
- Competitive edge through compliance-ready architectures.

3. Regulators & Market Surveillance Authorities

Obligations:
- Must oversee conformity assessments and enforce compliance across borders.
- Required to coordinate with sectoral regulators (e.g., UNECE, national transport authorities).
Risks:
- Fragmentation between AI Act and existing automotive regulations.
- Resource strain due to technical complexity and volume of AV deployments.
Opportunities:
- Development of harmonized standards and certification pathways.
- Use of regulatory sandboxes to test and refine oversight mechanisms.

4. Fleet Operators / Mobility-as-a-Service Providers

Obligations:
- Must ensure deployed AVs comply with AI Act and sectoral safety standards.
- Required to inform users about AI-driven decisions and ensure human oversight where applicable.
Risks:
- Operational liability for accidents or system failures.
- Public backlash if transparency and safety are lacking.
Opportunities:
- Ethical AV deployment can differentiate services and attract public support.
- Data-driven optimization of routes and maintenance.

5. Consumers / Road Users

Rights:
- Right to safety, transparency, and redress in case of harm.
- Protection from opaque or discriminatory AI decisions.
Risks:
- Potential for accidents due to system errors or edge-case failures.
- Privacy concerns from data collected by AVs (e.g., location, biometrics).
Opportunities:
- Safer, more accessible mobility options.
- Reduced human error and traffic fatalities.

🧭 Strategic Takeaway

The AI Act doesn’t operate in isolation—it intersects with existing automotive regulations, creating a hybrid compliance landscape. Stakeholders must navigate:

AI-specific obligations (e.g., bias mitigation, explainability)
Vehicle safety standards (e.g., UNECE, TAFR)
Data protection laws (e.g., GDPR for connected vehicle data)

Starting with a stakeholder matrix to map out responsibilities, risks, and opportunities, followed by a compliance roadmap tailored to autonomous vehicle (AV) deployment under the EU AI Act. This dual approach gives you both a strategic overview and an operational guide.

🚦 Autonomous Driving Stakeholder Matrix (EU AI Act)

Stakeholder	Responsibilities	Risks	Opportunities
Automotive OEMs	Ensure AI systems in AVs meet high-risk requirements; conduct conformity assessments	Liability for system failures; high compliance costs	Market leadership through ethical, compliant AVs
AI System Developers	Build explainable, robust, and traceable AI modules (e.g., perception, planning)	Technical complexity; explainability of deep learning models	Demand for modular, certifiable AI components
Fleet Operators / MaaS	Deploy compliant AVs; ensure user transparency and oversight	Operational liability; public trust erosion	Data-driven optimization; ethical mobility services
Regulators / Authorities	Monitor compliance; coordinate with transport and safety bodies	Fragmented oversight; resource strain	Harmonized standards; sandbox testing
Consumers / Road Users	Interact with AVs; exercise rights to safety, transparency, and redress	Privacy violations; algorithmic errors	Safer, more accessible transport; reduced human error

🛠️ Compliance Roadmap for AV Deployment under the EU AI Act

Phase 1: System Classification & Risk Assessment

Identify AI components (e.g., object detection, trajectory planning, driver monitoring).
Classify each system under the AI Act’s risk framework (most will be high-risk).
Conduct a Fundamental Rights Impact Assessment (FRIA) if deployed in public services.

Phase 2: Technical Documentation & Conformity Assessment

Prepare documentation covering:
- Intended purpose
- Training and validation data
- Risk management procedures
- Human oversight mechanisms
Choose conformity path:
- Internal control (for standard systems)
- Third-party assessment (for complex or novel systems)

Phase 3: Human Oversight & Explainability

Implement real-time monitoring and override capabilities.
Ensure outputs are interpretable by operators and regulators.
Train staff on AI system behavior and escalation protocols.

Phase 4: Post-Market Monitoring & Incident Reporting

Establish feedback loops for system performance and safety.
Report serious incidents or malfunctions to authorities within mandated timelines.
Update systems based on real-world data and evolving risks.

Phase 5: Transparency & User Rights

Inform users when interacting with AI (e.g., autonomous shuttles, ride-hailing AVs).
Provide mechanisms for contesting decisions or reporting harm.
Ensure compliance with GDPR for location, biometric, and behavioral data.

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Managing Artificial Intelligence Threats with ISO 27001

DISC InfoSec previous posts on AI category

Tags: AI Act, autonomous driving

Comments (0)

Aug 24 2025

Identify the rights of individuals affected by AI systems under the EU AI Act by doing a fundamental rights impact assessment (FRIA)

Category: AI — disc7 @ 9:52 pm

Great prompt, Hugh. The Fundamental Rights Impact Assessment (FRIA) under Article 27 of the EU AI Act is a powerful tool for identifying and protecting the rights of individuals affected by high-risk AI systems. Here’s how it works and what rights it safeguards:

🛡️ Key Rights Protected by the EU AI Act via FRIA

When conducting a FRIA, deployers must assess how an AI system could impact the following fundamental rights:

Right to human dignity
Ensures AI systems do not dehumanize or degrade individuals.
Right to non-discrimination
Protects against algorithmic bias based on race, gender, age, disability, etc.
Right to privacy and data protection
Evaluates how personal data is used, stored, and protected.
Freedom of expression and information
Ensures AI does not suppress speech or manipulate access to information.
Right to good administration
Guarantees fair, transparent, and accountable decision-making by public bodies using AI.
Access to justice and remedies
Individuals must be able to challenge decisions made by AI systems and seek redress.

🧾 What a FRIA Must Include

Deployers of high-risk AI systems (especially public bodies or private entities providing public services) must document:

Purpose and context of AI use
Groups likely to be affected
Specific risks of harm to those groups
Human oversight measures
Mitigation steps if risks materialize
Governance and complaint mechanisms

This assessment must be completed before first use and updated as needed. Results are reported to the market surveillance authority, and the EU AI Office will provide a standardized template.

🧭 Why It Matters

The FRIA isn’t just paperwork—it’s a safeguard against invisible harms. It forces organizations to think critically about how their AI systems might infringe on rights and to build in protections from the start. It’s a shift from reactive to proactive governance.

If you’d like, I can walk you through a mock FRIA for a specific AI use case—say, facial recognition in public spaces or automated hiring tools. Just name the scenario.

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Managing Artificial Intelligence Threats with ISO 27001

DISC InfoSec previous posts on AI category

Tags: Atricle 27, EU AI Act, FRIA

Comments (0)

Aug 23 2025

EU AI Act’s guidelines on ethical AI deployment in a scenario

Category: AI — disc7 @ 4:26 pm

Walk through a realistic scenario to interpret how the EU AI Act’s ethical guidelines would apply in practice.

🏥 Scenario: Deploying an AI System in a European Hospital

A hospital in Germany wants to deploy an AI system to assist doctors in diagnosing rare diseases based on patient data and medical imaging.

🧭 Applying the EU AI Act Guidelines

1. Risk Classification

The system is considered high-risk under the EU AI Act because it affects health outcomes and involves biometric data.
Therefore, it must meet strict requirements for transparency, robustness, and human oversight.

2. Ethical Deployment Requirements

Principle	Application in Scenario
Human Autonomy	Doctors retain final decision-making authority. AI provides recommendations, not verdicts.
Prevention of Harm	The system undergoes rigorous testing to avoid misdiagnosis. Fail-safes are built in.
Fairness & Non-Bias	Training data is audited to ensure diverse representation across age, gender, ethnicity.
Transparency	The hospital provides clear documentation on how the AI works and its limitations.
Explicability	Doctors can access explanations for each AI-generated diagnosis.
Accountability	The hospital sets up a governance board to monitor AI performance and handle complaints.

3. Compliance Measures

Data Governance: Patient data is anonymized and processed in line with GDPR.
Impact Assessment: A conformity assessment is conducted before deployment.
Monitoring & Reporting: The hospital commits to reporting serious incidents to the AI Office.
Stakeholder Engagement: Patients are informed and can opt out of AI-assisted diagnosis.

✅ Outcome

By following these steps, the hospital ensures that its AI system is ethically deployed, legally compliant, and trustworthy—aligning with the EU’s vision for responsible AI.

Explore how the EU AI Act’s ethical guidelines would apply in a real-world education scenario.

🎓 Scenario: AI-Powered Learning Analytics in a European Secondary School

A secondary school in France wants to use an AI system that analyzes student performance data to identify those at risk of falling behind and recommend personalized learning paths.

🧭 Applying the EU AI Act in Education

1. Risk Classification

This system is considered high-risk under the EU AI Act because it influences students’ access to educational opportunities and involves sensitive personal data.
Emotion-recognition features (e.g., analyzing facial expressions to gauge engagement) would be prohibited as they fall under the “unacceptable risk” category.

2. Ethical Deployment Requirements

Principle	How It Applies in the School Setting
Human Autonomy	Teachers make final decisions; AI offers insights, not mandates.
Fairness & Non-Bias	The system is trained on diverse datasets to avoid bias based on race, gender, or SES.
Transparency	Students and parents are informed about how the AI works and what data it uses.
Privacy Protection	Data is anonymized and processed in line with GDPR.
Accountability	The school designates a responsible staff member to oversee AI performance and ethics.
Explicability	Teachers can access clear explanations for each AI-generated recommendation.

3. Compliance Measures

Documentation: The school maintains records of the AI system’s design, training data, and risk assessments.
Monitoring: Regular audits are conducted to ensure the system remains fair and accurate.
Stakeholder Engagement: Students and parents can opt out and provide feedback on the system’s impact.

✅ Outcome

By following the EU AI Act’s guidelines, the school ensures that its AI system supports learning ethically, transparently, and safely—while respecting students’ rights and dignity.

You can explore the EU Commission’s educator guidelines for more practical advice on integrating AI into teaching and learning.

University-level scenario or explore how AI affects teacher evaluations.

Dive into a university-level scenario to see how the EU AI Act’s ethical guidelines shape the deployment of AI in higher education.

🎓 Scenario: AI-Based Admissions System at a European University

A university in the Netherlands wants to implement an AI system to help evaluate student applications for competitive graduate programs. The system analyzes academic records, recommendation letters, and personal statements to rank candidates.

🧭 Applying the EU AI Act in Higher Education

1. Risk Classification

This system is classified as high-risk under the EU AI Act because it significantly affects access to education and future opportunities.
It must comply with strict requirements around transparency, fairness, human oversight, and data governance.

2. Ethical Deployment Requirements

Principle	Application in University Admissions
Human Autonomy	Admissions officers retain final decision-making authority. AI provides rankings, not verdicts.
Fairness & Non-Bias	The system is trained on diverse, representative data to avoid bias based on gender, ethnicity, or socioeconomic status.
Transparency	Applicants are informed that AI is used and can request explanations of how decisions are made.
Privacy Protection	Personal data is processed in line with GDPR, with strict access controls and anonymization.
Accountability	The university appoints an AI ethics officer to monitor system performance and handle appeals.
Explicability	Admissions staff can access clear, interpretable explanations for each AI-generated recommendation.

3. Additional EU AI Act Provisions

Article 4: Requires that staff using the AI system receive training to ensure adequate AI literacy.
Recital 56: Encourages AI deployment that promotes high-quality digital education and critical thinking.
Emotion Recognition Ban: Any attempt to use emotion inference (e.g., analyzing facial expressions in video interviews) would be prohibited as an “unacceptable risk”.

✅ Outcome

By following the EU AI Act, the university ensures its admissions system is fair, transparent, and legally compliant, while respecting applicants’ rights and promoting trust in the process.

You can explore more in-depth guidance on AI in Higher Education from the European AI Alliance or read a detailed analysis in this Swiss Cyber Institute article.

Explore how universities can use AI for personalized learning or student support next.

EU AI Act’s ethical guidelines apply to a university scenario focused on personalized learning and student support.

🧑‍🎓 Scenario: AI-Powered Student Support System at a European University

A university in Spain deploys an AI system to monitor student engagement, predict academic risk, and recommend personalized resources—like tutoring, mental health services, or study groups.

🧭 EU AI Act Interpretation in This Context

1. Risk Classification

This system is considered high-risk because it influences students’ access to support services and may impact academic outcomes.
If it includes emotion recognition (e.g., analyzing facial expressions or voice tone), that feature is prohibited under the Act’s “unacceptable risk” category.

2. Ethical Deployment Requirements

Principle	Application in Student Support AI
Human Autonomy	Advisors and counselors retain control; AI offers suggestions, not decisions.
Fairness & Non-Bias	Algorithms are trained on diverse data to avoid disadvantaging marginalized groups.
Transparency	Students are informed about how the system works and what data it uses.
Privacy Protection	All personal data is anonymized and processed in compliance with GDPR.
Explicability	Staff can interpret why the AI flagged a student as needing support.
Accountability	The university sets up a governance board to audit system performance and ethics.

3. Additional EU AI Act Provisions

Article 4: Requires universities to ensure staff are trained in AI literacy, so they can use and supervise the system responsibly.
Recital 56: Encourages AI systems that promote high-quality digital education and empower students with critical thinking and media literacy.

✅ Outcome

By aligning with the EU AI Act, the university ensures its AI system enhances student well-being and academic success—while safeguarding rights, promoting fairness, and building trust.

If you’re curious about how universities are integrating these principles into real-world systems, check out this mapping of AI guidelines in higher education.

EU AI Act: Full text of the Artificial Intelligence Regulation

Practical OWASP Security Testing: Hands-On Strategies for Detecting and Mitigating Web Vulnerabilities in the Age of AI

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Managing Artificial Intelligence Threats with ISO 27001

DISC InfoSec previous posts on AI category

Tags: AI System in a European Hospital, scenario focused AI

Comments (0)

Aug 23 2025

Do you know what the primary objectives of the AI Act are?

Category: AI — disc7 @ 11:04 am

The EU AI Act is the European Union’s landmark regulation designed to create a legal framework for the development, deployment, and use of artificial intelligence across the EU. Its primary objectives can be summed up as follows:

Protect Fundamental Rights and Safety
1. Ensure AI systems do not undermine fundamental rights guaranteed by the EU Charter (privacy, non-discrimination, dignity, etc.) or compromise the health and safety of individuals.
Promote Trustworthy AI
1. Establish standards so AI systems are transparent, explainable, and accountable, which is key to building public trust in AI adoption.
Risk-Based Regulation
1. Introduce a tiered approach:
  1. Unacceptable risk: Prohibit AI uses that pose clear threats (e.g., social scoring by governments, manipulative systems).
  1. High risk: Strict obligations for AI in sensitive areas like healthcare, finance, employment, and law enforcement.
  1. Limited/minimal risk: Light or no regulatory requirements.
Harmonize AI Rules Across the EU
1. Create a uniform framework that avoids fragmented national laws, ensuring legal certainty for businesses operating in multiple EU countries.
Foster Innovation and Competitiveness
1. Encourage AI innovation by providing clear rules and setting up “regulatory sandboxes” where businesses can test AI in a supervised, low-risk environment.
Ensure Transparency for Users
1. Require disclosure when people interact with AI (e.g., chatbots, deepfakes) so users know they are dealing with a machine.
Strengthen Governance and Oversight
1. Establish national supervisory authorities and an EU-level AI Office to monitor compliance, enforce rules, and coordinate among Member States.
Address Bias and Discrimination
1. Mandate quality datasets, documentation, and testing to reduce harmful bias in AI systems, particularly in areas affecting citizens’ rights and opportunities.
Guarantee Robustness and Cybersecurity
1. Require that AI systems are secure, resilient against attacks or misuse, and perform reliably across their lifecycle.
Global Standard Setting
Position the EU as a leader in setting international norms for AI regulation, influencing global markets the way GDPR did for privacy.

understand the scope of the AI Act.

To understand the scope of the EU AI Act, it helps to break it down into who and what it applies to, and how risk determines obligations. Here’s a clear guide:

1. Who it Applies To

Providers: Anyone (companies, developers, public bodies) placing AI systems on the EU market, regardless of where they are based.
Deployers/Users: Organizations or individuals using AI within the EU.
Importers & Distributors: Those selling or distributing AI systems in the EU.

➡️ Even if a company is outside the EU, the Act applies if their AI systems are used in the EU.

2. What Counts as AI

The Act uses a broad definition of AI (based on OECD/Commission standards).
Covers systems that can:
- process data,
- generate outputs (predictions, recommendations, decisions),
- influence physical or virtual environments.
Includes machine learning, rule-based, statistical, and generative AI models.

3. Risk-Based Approach

The scope is defined by categorizing AI uses into risk levels:

Unacceptable Risk (Prohibited)
- Social scoring, manipulative techniques, real-time biometric surveillance in public (with limited exceptions).
High Risk (Strictly Regulated)
- AI in sensitive areas like:
  - healthcare (diagnostics, medical devices),
  - employment (CV screening),
  - education (exam scoring),
  - law enforcement and migration,
  - critical infrastructure (transport, energy).
Limited Risk (Transparency Requirements)
- Chatbots, deepfakes, emotion recognition—users must be informed they are interacting with AI.
Minimal Risk (Largely Unregulated)
- AI in spam filters, video games, recommendation engines—free to operate with voluntary best practices.

4. Exemptions

AI used for military and national security is outside the Act’s scope.
Systems used solely for research and prototyping are exempt until they are placed on the market.

5. Key Takeaway on Scope

The EU AI Act is horizontal (applies across sectors) but graduated (the rules depend on risk).

If you are a provider, you need to check whether your system falls into a prohibited, high, limited, or minimal category.
If you are a user, you need to know what obligations apply when deploying AI (especially if it’s high-risk).

👉 In short: The scope of the EU AI Act is broad, extraterritorial, and risk-based. It applies to almost anyone building, selling, or using AI in the EU, but the depth of obligations depends on how risky the AI application is considered.

EU AI Act: Full text of the Artificial Intelligence Regulation

Practical OWASP Security Testing: Hands-On Strategies for Detecting and Mitigating Web Vulnerabilities in the Age of AI

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Managing Artificial Intelligence Threats with ISO 27001

DISC InfoSec previous posts on AI category

Tags: EU AI Act

Comments (0)

Aug 21 2025

ISO/IEC 42001 Requirements Mapped to ShareVault

Category: AI,Information Security — disc7 @ 2:55 pm

🏢 Strategic Benefits for ShareVault

Regulatory Alignment: ISO 42001 supports GDPR, HIPAA, and EU AI Act compliance.
Client Trust: Demonstrates responsible AI governance to enterprise clients.
Competitive Edge: Positions ShareVault as a forward-thinking, standards-compliant VDR provider.
Audit Readiness: Facilitates internal and external audits of AI systems and data handling.

If ShareVault were to pursue ISO 42001 certification, it would not only strengthen its AI governance but also reinforce its reputation in regulated industries like life sciences, finance, and legal services.

Here’s a tailored ISO/IEC 42001 implementation roadmap for a Virtual Data Room (VDR) provider like ShareVault, focusing on responsible AI governance, risk mitigation, and regulatory alignment.

🗺️ ISO/IEC 42001 Implementation Roadmap for ShareVault

Phase 1: Initiation & Scoping

🔹 Objective: Define the scope of AI use and align with business goals.

Identify AI-powered features (e.g., smart search, document tagging, access analytics).
Map stakeholders: internal teams, clients, regulators.
Define scope of the AI Management System (AIMS): which systems, processes, and data are covered.
Appoint an AI Governance Lead or Steering Committee.

Phase 2: Gap Analysis & Risk Assessment

🔹 Objective: Understand current state vs. ISO 42001 requirements.

Conduct a gap analysis against ISO 42001 clauses.
Evaluate risks related to:
- Data privacy (e.g., GDPR, HIPAA)
- Bias in AI-driven document classification
- Misuse of access analytics
Review existing controls and identify vulnerabilities.

Phase 3: Policy & Governance Framework

🔹 Objective: Establish foundational policies and oversight mechanisms.

Draft an AI Policy aligned with ethical principles and legal obligations.
Define roles and responsibilities for AI oversight.
Create procedures for:
- Human oversight and intervention
- Incident reporting and escalation
- Lifecycle management of AI models

Phase 4: Data & Model Governance

🔹 Objective: Ensure trustworthy data and model practices.

Implement controls for training and testing data quality.
Document data sources, preprocessing steps, and validation methods.
Establish model documentation standards (e.g., model cards, audit trails).
Define retention and retirement policies for outdated models.

Phase 5: Operational Controls & Monitoring

🔹 Objective: Embed AI governance into daily operations.

Integrate AI risk controls into DevOps and product workflows.
Set up performance monitoring dashboards for AI features.
Enable logging and traceability of AI decisions.
Conduct regular internal audits and reviews.

Phase 6: Stakeholder Engagement & Transparency

🔹 Objective: Build trust with users and clients.

Communicate AI capabilities and limitations clearly in the UI.
Provide opt-out or override options for AI-driven decisions.
Engage clients in defining acceptable AI behavior and use cases.
Train staff on ethical AI use and ISO 42001 principles.

Phase 7: Certification & Continuous Improvement

🔹 Objective: Achieve compliance and evolve responsibly.

Prepare documentation for ISO 42001 certification audit.
Conduct mock audits and address gaps.
Establish feedback loops for continuous improvement.
Monitor regulatory changes (e.g., EU AI Act, U.S. AI bills) and update policies accordingly.

🧠 Bonus Tip: Align with Other Standards

ShareVault can integrate ISO 42001 with:

ISO 27001 (Information Security)
ISO 9001 (Quality Management)
SOC 2 (Trust Services Criteria)
EU AI Act (for high-risk AI systems)

visual roadmap for implementing ISO/IEC 42001 tailored to a Virtual Data Room (VDR) provider like ShareVault:

🗂️ ISO 42001 Implementation Roadmap for VDR Providers

Each phase is mapped to a monthly milestone, showing how AI governance can be embedded step-by-step:

📌 Milestone Highlights

Month 1 – Initiation & Scoping Define AI use cases (e.g., smart search, access analytics), map stakeholders, appoint governance lead.
Month 2 – Gap Analysis & Risk Assessment Evaluate risks like bias in document tagging, privacy breaches, and misuse of analytics.
Month 3 – Policy & Governance Framework Draft AI policy, define oversight roles, and create procedures for human intervention and incident handling.
Month 4 – Data & Model Governance Implement controls for training data, document model behavior, and set retention policies.
Month 5 – Operational Controls & Monitoring Embed governance into workflows, monitor AI performance, and conduct internal audits.
Month 6 – Stakeholder Engagement & Transparency Communicate AI capabilities to users, engage clients in ethical discussions, and train staff.
Month 7 – Certification & Continuous Improvement Prepare for ISO audit, conduct mock assessments, and monitor evolving regulations like the EU AI Act.

Practical OWASP Security Testing: Hands-On Strategies for Detecting and Mitigating Web Vulnerabilities in the Age of AI

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Managing Artificial Intelligence Threats with ISO 27001

Tags: ISO 42001, Sharevault

Comments (0)

Aug 21 2025

How to Classify an AI system into one of the categories: unacceptable risk, high risk, limited risk, minimal or no risk.

Category: AI,Information Classification — disc7 @ 1:25 pm

🔹 1. Unacceptable Risk (Prohibited AI)

These are AI practices banned outright because they pose a clear threat to safety, rights, or democracy.
Examples:

Social scoring by governments (like assigning citizens a “trust score”).
Real-time biometric identification in public spaces for mass surveillance (with narrow exceptions like serious crime).
Manipulative AI that exploits vulnerabilities (e.g., toys with voice assistants that encourage dangerous behavior in kids).

👉 If your system falls here → cannot be marketed or used in the EU.

🔹 2. High Risk

These are AI systems with significant impact on people’s rights, safety, or livelihoods. They are allowed but subject to strict compliance (risk management, testing, transparency, human oversight, etc.).
Examples:

AI in recruitment (CV screening, job interview analysis).
Credit scoring or AI used for approving loans.
Medical AI (diagnosis, treatment recommendations).
AI in critical infrastructure (electricity grid management, transport safety systems).
AI in education (grading, admissions decisions).

👉 If your system is high-risk → must undergo conformity assessment and registration before use.

🔹 3. Limited Risk

These require transparency obligations, but not full compliance like high-risk systems.
Examples:

Chatbots (users must know they’re talking to AI, not a human).
AI systems generating deepfakes (must disclose synthetic nature unless for law enforcement/artistic/expressive purposes).
Emotion recognition systems in non-high-risk contexts.

👉 If limited risk → inform users clearly, but lighter obligations.

🔹 4. Minimal or No Risk

The majority of AI applications fall here. They’re largely unregulated beyond general EU laws.
Examples:

Spam filters.
AI-powered video games.
Recommendation systems for e-commerce or music streaming.
AI-driven email autocomplete.

👉 If minimal/no risk → free use with no extra requirements.

⚖️ Rule of Thumb for Classification:

If it manipulates or surveils → often unacceptable risk.
If it affects health, jobs, education, finance, safety, or fundamental rights → high risk.
If it interacts with humans but without major consequences → limited risk.
If it’s just convenience or productivity-related → minimal/no risk.

A decision tree you can use to classify any AI system under the EU AI Act risk framework:

🧭 EU AI Act AI System Risk Classification Decision Tree

Step 1: Check for Prohibited Practices

👉 Does the AI system do any of the following?

Social scoring of individuals by governments or large-scale ranking of citizens?
Manipulative AI that exploits vulnerable groups (e.g., children, disabled, addicted)?
Real-time biometric identification in public spaces (mass surveillance), except for narrow law enforcement use?
Subliminal manipulation that harms people?

✅ Yes → UNACCEPTABLE RISK (Prohibited, not allowed in EU).
❌ No → go to Step 2.

Step 2: Check for High-Risk Use Cases

👉 Does the AI system significantly affect people’s safety, rights, or livelihoods, such as:

Biometrics (facial recognition, identification, sensitive categorization)?
Education (grading, admissions, student assessment)?
Employment (recruitment, CV screening, promotion decisions)?
Essential services (credit scoring, access to welfare, healthcare)?
Law enforcement & justice (predictive policing, evidence analysis, judicial decision support)?
Critical infrastructure (transport, energy, water, safety systems)?
Medical devices or health AI (diagnosis, treatment recommendations)?

✅ Yes → HIGH RISK (Strict obligations: conformity assessment, risk management, registration, oversight).
❌ No → go to Step 3.

Step 3: Check for Transparency Requirements (Limited Risk)

👉 Does the AI system:

Interact with humans in a way that users might think they are talking to a human (e.g., chatbot, voice assistant)?
Generate or manipulate content that could be mistaken for real (e.g., deepfakes, synthetic media)?
Use emotion recognition or biometric categorization outside high-risk cases?

✅ Yes → LIMITED RISK (Transparency obligations: disclose AI use to users).
❌ No → go to Step 4.

Step 4: Everything Else

👉 Is the AI system just for convenience, productivity, personalization, or entertainment without major societal or legal impact?

✅ Yes → MINIMAL or NO RISK (Free use, no extra regulation).

⚖️ Quick Classification Examples:

Social scoring AI → ❌ Unacceptable Risk
AI for medical diagnosis → 🚨 High Risk
AI chatbot for customer service → ⚠️ Limited Risk
Spam filter / recommender system → ✅ Minimal Risk

Practical OWASP Security Testing: Hands-On Strategies for Detecting and Mitigating Web Vulnerabilities in the Age of AI

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Expertise-in-Virtual-CISO-vCISO-Services-2 Download

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Tags: AI categories, AI Sytem, EU AI Act

Comments (0)

Aug 20 2025

The highlights from the OWASP AI Maturity Assessment framework

Category: AI,owasp — disc7 @ 3:51 pm

1. Purpose and Scope

The OWASP AI Maturity Assessment provides organizations with a structured way to evaluate how mature their practices are in managing the security, governance, and ethical use of AI systems. Its scope goes beyond technical safeguards, emphasizing a holistic approach that covers people, processes, and technology.

2. Core Maturity Domains

The framework divides maturity into several domains: governance, risk management, security, compliance, and operations. Each domain contains clear criteria that organizations can use to assess themselves and identify both strengths and weaknesses in their AI security posture.

3. Governance and Oversight

A strong governance foundation is highlighted as essential. This includes defining roles, responsibilities, and accountability structures for AI use, ensuring executive alignment, and embedding oversight into organizational culture. Without governance, technical controls alone are insufficient.

4. Risk Management Integration

Risk management is emphasized as an ongoing process that must be integrated into AI lifecycles. This means continuously identifying, assessing, and mitigating risks associated with data, algorithms, and models, while also accounting for evolving threats and regulatory changes.

5. Security and Technical Controls

Security forms a major part of the maturity model. It stresses the importance of secure coding, model hardening, adversarial resilience, and robust data protection. Secure development pipelines and automated monitoring of AI behavior are seen as crucial for preventing exploitation.

6. Compliance and Ethical Considerations

The assessment underscores regulatory alignment and ethical responsibilities. Organizations are expected to demonstrate compliance with applicable laws and standards while ensuring fairness, transparency, and accountability in AI outcomes. This dual lens of compliance and ethics sets the framework apart.

7. Operational Excellence

Operational maturity is measured by how well organizations integrate AI governance into day-to-day practices. This includes ongoing monitoring of deployed AI systems, clear incident response procedures for AI failures or misuse, and mechanisms for continuous improvement.

8. Maturity Levels

The framework uses levels of maturity (from ad hoc practices to fully optimized processes) to help organizations benchmark themselves. Moving up the levels involves progress from reactive, fragmented practices to proactive, standardized, and continuously improving capabilities.

9. Practical Assessment Method

The assessment is designed to be practical and repeatable. Organizations can self-assess or engage third parties to evaluate maturity against OWASP criteria. The output is a roadmap highlighting gaps, recommended improvements, and prioritized actions based on risk appetite.

10. Value for Organizations

Ultimately, the OWASP AI Maturity Assessment enables organizations to transform AI adoption from a risky endeavor into a controlled, strategic advantage. By balancing governance, security, compliance, and ethics, it gives organizations confidence in deploying AI responsibly at scale.

My Opinion

The OWASP AI Maturity Assessment stands out as a much-needed framework in today’s AI-driven world. Its strength lies in combining technical security with governance and ethics, ensuring organizations don’t just “secure AI” but also use it responsibly. The maturity levels provide clear benchmarks, making it actionable rather than purely theoretical. In my view, this framework can be a powerful tool for CISOs, compliance leaders, and AI product managers who need to align innovation with trust and accountability.

**visual roadmap** of the OWASP AI Maturity levels (1–5), showing the progression from ad hoc practices to fully optimized, proactive, and automated AI governance and security.

Download OWASP AI Maturity Assessment Ver 1.0 August 11, 2025

PDF of the OWASP AI Maturity Roadmap with business-value highlights for each level.

OWASP_AI_Maturity_Roadmap Download

Practical OWASP Security Testing: Hands-On Strategies for Detecting and Mitigating Web Vulnerabilities in the Age of AI

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Expertise-in-Virtual-CISO-vCISO-Services-2 Download

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Tags: OWASP AI Maturity, OWASP Security Testing

Comments (0)

Aug 19 2025

Geoffrey Hinton Warns: Why AI Needs a ‘Mother’ to Stay Under Control

Category: AI — disc7 @ 10:02 am

1. A Critical Voice in a Transformative Moment

At the AI4 2025 conference in Las Vegas, Geoffrey Hinton—renowned as the “Godfather of AI” and a Nobel Prize winner—issued a powerful warning about the trajectory of artificial intelligence. Speaking to an audience of over 8,000 tech leaders, researchers, and policymakers, Hinton emphasized that while AI’s capabilities are expanding rapidly, we’re lacking the global coordination needed to manage it safely.

2. The Rise of Fragmented Intelligence

Hinton highlighted how AI is being deployed across diverse sectors—healthcare, transportation, finance, and military systems. Each application grows more autonomous, yet most are developed in isolation. This fragmented evolution, he argued, increases the risk of incompatible systems, competing goals, and unintended consequences—ranging from biased decisions to safety failures.

3. Introducing the Concept of “Mother AI”

To address this fragmentation, Hinton proposed a controversial but compelling idea: a centralized supervisory intelligence, which he dubbed “Mother AI.” This system would act as a layer of governance above all other AIs, helping to coordinate their behavior, ensure ethical standards, and maintain alignment with human values.

4. A Striking Analogy

Hinton used a vivid metaphor to describe this supervisory model: “The only example of a more intelligent being being controlled by a less intelligent one is a mother being controlled by her baby.” In this analogy, individual AIs are the children—powerful yet immature—while “Mother AI” provides the wisdom, discipline, and ethical guidance necessary to keep them in check.

5. Ethics, Oversight, and Coordination

The key role of this Mother AI, according to Hinton, would be to serve as a moral and operational compass. It would enforce consistency across various systems, prevent destructive behavior, and address the growing concern that AI systems might evolve in ways that humans cannot predict or control. Such oversight would help mitigate risks like surveillance misuse, algorithmic bias, or even accidental harm.

6. Innovation vs. Control

Despite his warnings, Hinton acknowledged AI’s immense benefits—particularly in areas like medicine, where it could revolutionize diagnostics, personalize treatments, and even cure previously untreatable diseases. His core argument wasn’t to slow progress, but to steer it—ensuring innovation is paired with global governance to avoid reckless development.

7. The Bigger Picture

Hinton’s call for a unifying AI framework is a challenge to the current laissez-faire approach in the tech industry. His concept of a “Mother AI” is less about creating a literal super-AI and more about instilling centralized accountability in a world of distributed algorithms. The broader implication: if we don’t proactively guide AI’s development, it may evolve in ways that slip beyond our control.

My Opinion

Hinton’s proposal is bold, thought-provoking, and increasingly necessary. The idea of a “Mother AI” might sound dramatic, but it reflects a deep truth: today’s AI systems are being built faster than society can regulate or understand them. While the metaphor may not translate into a practical solution immediately, it effectively underscores the urgent need for coordination, oversight, and ethical alignment. Without that, we risk building a powerful ecosystem of machines that may not share—or even recognize—our values. The future of AI isn’t just about intelligence; it’s about wisdom, and that starts with humans taking responsibility now…

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Tags: Mother AI

Comments (0)

Aug 18 2025

AI-Driven Hacking: The New Frontier in Cybersecurity

Category: AI,Hacking,Information Security — disc7 @ 10:02 am

The age of AI-assisted hacking is no longer looming—it’s here. Hackers of all stripes—from state actors to cybercriminals—are now integrating AI tools into their operations, while defenders are racing to catch up.

Key Developments

In mid‑2025, Russian intelligence reportedly sent phishing emails to Ukrainians containing AI-powered attachments that automatically scanned victims’ computers for sensitive files and transmitted them back to Russia. NBC Bay Area
AI models like ChatGPT have become highly adept at translating natural language into code, helping hackers automate their work and scale operations. NBC Bay Area
AI hasn’t ushered in a hacking revolution that enables novices to bring down power grids—but it is significantly enhancing the efficiency and reach of skilled hackers. NBC Bay Area

On the Defensive Side

Cybersecurity defenders are also turning to AI—Google’s “Gemini” model helped identify over 20 software vulnerabilities, speeding up bug detection and patching.
Alexei Bulazel of the White House’s National Security Council believes defenders currently hold a slight edge over attackers, thanks to America’s tech infrastructure, but that balance may shift as agentic (autonomous) AI tools proliferate.
A notable milestone: an AI called “Xbow” topped the HackerOne leaderboard, prompting the platform to create a separate category for AI-generated hacking tools.

My Take

This article paints a vivid picture of an escalating AI arms race in cybersecurity. My view? It’s a dramatic turning point:

AI is already tipping the scale—but not overwhelmingly. Hackers are more efficient, but full-scale automated digital threats haven’t arrived. Still, what used to require deep expertise is becoming accessible to more people.
Defenders aren’t standing idle. AI-assisted scanning and rapid vulnerability detection are powerful tools in the white-hat arsenal—and may remain decisive, especially when backed by robust tech ecosystems.
The real battleground is trust. As AI makes exploits more sophisticated and deception more believable (e.g., deepfakes or phishing), trust becomes the most vulnerable asset. This echoes broader reports showing attacks are increasingly AI‑powered, whether via deceptive audio/video or tailored phishing campaigns.
Vigilance must evolve. Automated defenses and rapid detection will be key. Organizations should also invest in digital literacy—training humans to recognize deception even as AI tools become ever more convincing.

In Summary

AI is enhancing both hacking and defense—but it’s not yet an apocalyptic breakthrough.
Skilled attackers can now move faster and more subtly.
Defenders have powerful AI tools in their corner—but must remain agile.
As deception scales, safeguarding trust and awareness is crucial.

Master AI Tools Like ChatGPT and MidJourney to Automate Tasks, Generate Content, and Stay Ahead in the Digital Age

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Tags: AI-Driven Hacking, Generative AI Hacks

Comments (0)

Aug 17 2025

The CISO 3.0: A Guide to Next-Generation Cybersecurity Leadership

Category: CISO,Information Security,vCISO — disc7 @ 2:31 pm

The CISO 3.0: A Guide to Next-Generation Cybersecurity Leadership – Security, Audit and Leadership Series is out by Walt Powell.

This book positions itself not just as a technical guide but as a strategic roadmap for the future of cybersecurity leadership. It emphasizes that in today’s complex threat environment, CISOs must evolve beyond technical mastery and step into the role of business leaders who weave cybersecurity into the very fabric of organizational strategy.

The core message challenges the outdated view of CISOs as purely technical experts. Instead, it calls for a strategic shift toward business alignment, measurable risk management, and adoption of emerging technologies like AI and machine learning. This evolution reflects growing expectations from boards, executives, and regulators—expectations that CISOs must now meet with business fluency, not just technical insight.

The book goes further by offering actionable guidance, case studies, and real-world examples drawn from extensive experience across hundreds of security programs. It explores practical topics such as risk quantification, cyber insurance, and defining materiality, filling the gap left by more theory-heavy resources.

For aspiring CISOs, the book provides a clear path to transition from technical expertise to strategic leadership. For current CISOs, it delivers fresh insight into strengthening business acumen and boardroom credibility, enabling them to better drive value while protecting organizational assets.

My thought: This book’s strength lies in recognizing that the modern CISO role is no longer just about defending networks but about enabling business resilience and trust. By blending strategy with technical depth, it seems to prepare security leaders for the boardroom-level influence they now require. In an era where cybersecurity is a business risk, not just an IT issue, this perspective feels both timely and necessary.

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Tags: CISO 3.0

Comments (1)

Aug 17 2025

Benefits and drawbacks of using open-source models versus closed-source models under the AI Act

Category: AI,Information Security — disc7 @ 1:36 pm

Objectives of EU AI Act is:

Harmonized rules for AI systems in the EU, prohibitions on certain AI practices, requirements for high
risk AI, transparency rules, market surveillance, and innovation support.

1. Overview: How the AI Act Treats Open-Source vs. Closed-Source Models

The EU AI Act (formalized in 2024) regulates AI systems using a risk-based framework that ranges from unacceptable to minimal risk. It also includes a specific layer for general-purpose AI (GPAI)—“foundation models” like large language models.
Open-source models enjoy limited exemptions, especially if:
- They’re not high-risk,
- Not unsafe or interacting directly with individuals,
- Not monetized,
- Or not deemed to present systemic risk.
Closed-source (proprietary) models don’t benefit from such leniency and must comply with all applicable obligations across risk categories.

2. Benefits of Open-Source Models under the AI Act

a) Greater Transparency & Documentation

Open-source code, weights, and architecture are accessible by default—aligning with transparency expectations (e.g., model cards, training data logs)—and often already publicly documented.
Independent auditing becomes more feasible through community visibility.
A Stanford study found open-source models tend to comply more readily with data and compute transparency requirements than closed-source alternatives.

b) Lower Compliance Burden (in Certain Cases)

Exemptions: Non-monetized open-source models that don’t pose systemic risk may dodge burdensome obligations like documentation or designated representatives.
For academic or purely scientific purposes, there’s additional leniency—even if models are open-source.

c) Encourages Innovation, Collaboration & Inclusion

Open-source democratizes AI access, reducing barriers for academia, startups, nonprofits, and regional players.
Wider collaboration speeds up innovation and enables localization (e.g., fine-tuning for local languages or use cases).
Diverse contributors help surface bias and ethical concerns, making models more inclusive.

3. Drawbacks of Open-Source under the AI Act

a) Disproportionate Regulatory Burden

The Act’s “one-size-fits-all” approach imposes heavy requirements (like ten-year documentation, third-party audits) even on decentralized, collectively developed models—raising feasibility concerns.
Who carries responsibility in distributed, open environments remains unclear.

b) Loopholes and Misuse Risks

The Act’s light treatment of non-monetized open-source models could be exploited by malicious actors to skirt regulations.
Open-source models can be modified or misused to generate disinformation, deepfakes, or hate content—without safeguards that closed systems enforce.

c) Still Subject to Core Obligations

Even under exemptions, open-source GPAI must still:
- Disclose training content,
- Respect EU copyright laws,
- Possibly appoint authorized representatives if systemic risk is suspected.

d) Additional Practical & Legal Complications

Licensing: Some so-called “open-source” models carry restrictive terms (e.g., commercial restrictions, copyleft provisions) that may hinder compliance or downstream use.
Support disclaimers: Open-source licenses typically disclaim warranties—risking liability gaps.
Security vulnerabilities: Public availability of code may expose models to tampering or release of harmful versions.

4. Closed-Source Models: Benefits & Drawbacks

Benefits

Able to enforce usage restrictions, internal safety mechanisms, and fine-grained control over deployment—reducing misuse risk.
Clear compliance path: centralized providers can manage documentation, audits, and risk mitigation systematically.
Stable liability chain, with better alignment to legal frameworks.

Drawbacks

Less transparency: core workings are hidden, making audits and oversight harder.
Higher compliance burden: must meet all applicable obligations across risk categories without the possibility of exemptions.
Innovation lock-in: smaller players and researchers may face high entry barriers.

5. Synthesis: Choosing Between Open-Source and Closed-Source under the AI Act

Dimension	Open-Source	Closed-Source
Transparency & Auditing	High—code, data, model accessible	Low—black box systems
Regulatory Burden	Lower for non-monetized, low-risk models; heavy for complex, high-risk cases	Uniformly high, though manageable by central entities
Innovation & Accessibility	High—democratizes access, collaboration	Limited—controlled by large orgs
Security & Misuse Risk	Higher—modifiable, misuse easier	Lower—safeguarded, controlled deployment
Liability & Accountability	Diffuse—decentralized contributors complicate oversight	Clear—central authority responsible

6. Final Thoughts

Under the EU AI Act, open-source AI is recognized and, in some respects, encouraged—but only under narrow, carefully circumscribed conditions. When models are non-monetized, low-risk, or aimed at scientific research, open-source opens up paths for innovation. The transparency and collaborative dynamics are strong virtues.

However, when open-source intersects with high risk, monetization, or systemic potential, the Act tightens its grip—subjecting models to many of the same obligations as proprietary ones. Worse, ambiguity in responsibility and enforcement may undermine both innovation and safety.

Conversely, closed-source models offer regulatory clarity, security, and control; but at the cost of transparency, higher compliance burden, and restricted access for smaller players.

TL;DR

Choose open-source if your goal is transparency, inclusivity, and innovation—so long as you keep your model non-monetized, transparently documented, and low-risk.
Choose closed-source when safety, regulatory oversight, and controlled deployment are paramount, especially in sensitive or high-risk applications.

Further reading on EU AI Act implications

https://www.barrons.com/articles/ai-tech-stocks-regulation-microsoft-google-amazon-meta-30424359?

https://apnews.com/article/a3df6a1a8789eea7fcd17bffc750e291?

The European Union flag stands inside the atrium at the European Council building in Brussels, June 17, 2024. (AP Photo/Omar Havana, file)

Agentic AI Security Risks: Why Enterprises Can’t Afford to Fly Blind

NIST Strengthens Digital Identity Security to Tackle AI-Driven Threats

Securing Agentic AI: Emerging Risks and Governance Imperatives

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Expertise-in-Virtual-CISO-vCISO-Services-2 Download

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Tags: open-source models versus closed-source models under the AI Act

Comments (0)

Aug 15 2025

Agentic AI Security Risks: Why Enterprises Can’t Afford to Fly Blind

Category: AI — disc7 @ 1:44 pm

Introduction: The Double-Edged Sword of Agentic AI

The adoption of agentic AI is accelerating, promising unprecedented automation, operational efficiency, and innovation. But without robust security controls, enterprises are venturing into a high-risk environment where traditional cybersecurity safeguards no longer apply. These risks go far beyond conventional threat models and demand new governance, oversight, and technical protections.

1. Autonomous Misbehavior and Operational Disruption

Agentic AI systems can act without human intervention, making real-time decisions in business-critical environments. Without precise alignment and defined boundaries, these systems could:

Overwrite or delete critical data
Make unauthorized purchases or trigger processes
Misconfigure environments or applications
Interact with employees or customers in unintended ways

Business Impact: This can lead to costly downtime, compliance violations, and serious reputational damage. The unpredictable nature of autonomous agents makes operational resilience planning essential.

2. Regulatory Compliance Failures

Agentic AI introduces unique compliance risks that go beyond common IT governance issues. Misconfigured or unmonitored systems can violate:

Privacy laws such as GDPR or HIPAA
Financial regulations like SOX or PCI-DSS
Emerging AI-specific laws like the EU AI Act

Business Impact: These violations can trigger heavy fines, legal disputes, and delayed AI-driven product launches due to failed audits or remediation needs.

3. Shadow AI and Unmanaged Access

The rapid growth of shadow AI—unapproved, employee-deployed AI tools—creates an invisible attack surface. Examples include:

Public LLM agents granted internal system access
Code-generating agents deploying unvetted scripts
Plugin-enabled AI tools interacting with production APIs

Business Impact: These unmanaged agents can serve as hidden backdoors, leaking sensitive data, exposing credentials, or bypassing logging and authentication controls.

4. Data Exposure Through Autonomous Agents

When agentic AI interacts with public tools or plugins without oversight, data leakage risks multiply. Common scenarios include:

AI agents sending confidential data to public LLMs
Automated code execution revealing proprietary logic
Bypassing existing DLP (Data Loss Prevention) controls

Business Impact: Unauthorized data exfiltration can result in IP theft, compliance failures, and loss of customer trust.

5. Supply Chain and Partner Vulnerabilities

Autonomous agents often interact with third-party systems, APIs, and vendors, which creates supply chain risks. A misconfigured agent could:

Propagate malware via insecure APIs
Breach partner data agreements
Introduce liability into downstream environments

Business Impact: Such incidents can erode strategic partnerships, cause contractual disputes, and damage market credibility.

Conclusion: Agentic AI Needs First-Class Security Governance

The speed of agentic AI adoption means enterprises must embed security into the AI lifecycle—not bolt it on afterward. This includes:

Governance frameworks for AI oversight
Continuous monitoring and risk assessment
Phishing-resistant authentication and access controls
Cross-functional collaboration between security, compliance, and operational teams

My Take: Agentic AI can be a powerful competitive advantage, but unmanaged, it can also act as an unpredictable insider threat. Enterprises should approach AI governance with the same seriousness as financial controls—because in many ways, the risks are even greater.

Agentic AI: Navigating Risks and Security Challenges:

Securing Agentic AI: Emerging Risks and Governance Imperatives

State of Agentic AI Security and Governance

Three Essentials for Agentic AI Security

Is Agentic AI too advanced for its own good?

Mastering Agentic AI: Building Autonomous AI Agents with LLMs, Reinforcement Learning, and Multi-Agent Systems

DISC InfoSec previous posts on AI category

Artificial Intelligence Hacks

Managing Artificial Intelligence Threats with ISO 27001

ISO 42001 Foundation – Master the fundamentals of AI governance.

ISO 42001 Lead Auditor – Gain the skills to audit AI Management Systems.

ISO 42001 Lead Implementer – Learn how to design and implement AIMS.

Accredited by ANSI National Accreditation Board (ANAB) through PECB, ensuring global recognition.

Are you ready to lead in the world of AI Management Systems? Get certified in ISO 42001 with our exclusive 20% discount on top-tier e-learning courses – including the certification exam!

Limited-time offer – Don’t miss out! Contact us today to secure your spot.

Tags: Agentic AI Security Risks

Comments (1)

Aug 15 2025

NIST Strengthens Digital Identity Security to Tackle AI-Driven Threats

Category: AI,NIST Privacy — disc7 @ 9:27 am

The US National Institute of Standards and Technology (NIST) has issued its first major update to the Digital Identity Guidelines since 2017, responding to new cybersecurity challenges such as AI-enhanced phishing, deepfake fraud, and evolving identity attacks. The revision reflects how digital identity threats have grown more sophisticated and how organizations must adapt both technically and operationally to counter them.

The updated guidelines combine technical specifications and organizational recommendations to strengthen identity and access management (IAM) practices. While some elements refine existing methods, others introduce a fundamentally different approach to authentication and risk management, encouraging broader adoption of phishing-resistant and fraud-aware security measures.

A major focus is on AI-driven attack vectors. Advances in artificial intelligence have made phishing harder to detect, while deepfakes and synthetic identities challenge traditional identity verification processes. Although passwordless authentication, such as passkeys, offers a promising solution, adoption has been slowed by integration and compatibility hurdles. NIST now emphasizes stronger fraud detection, media forgery detection, and the use of FIDO-based phishing-resistant authentication.

This revision—NIST Special Publication 800-63, Revision 4—is the result of nearly four years of research, public drafts, and feedback from about 6,000 comments. It addresses identity proofing, authentication, and federation requirements, aiming to enhance security, privacy, and user experience. Importantly, it positions identity management as a shared responsibility, engaging cybersecurity, privacy, usability, program integrity, and mission operations teams in coordinated governance.

Key updates include revised risk management processes, continuous evaluation metrics, expanded fraud prevention measures, restructured identity proofing controls with clearer roles, safeguards against injection attacks and forged media, support for synced authenticators like passkeys, recognition of subscriber-controlled wallets, and updated password rules. These additions aim to balance robust protection with usability.

Overall, the revision represents a strategic shift from the previous edition, incorporating lessons from real-world breaches and advancements in identity technology. By setting a more comprehensive and collaborative framework, NIST aims to help organizations make digital interactions safer, more trustworthy, and more user-friendly while maintaining strong defenses against rapidly evolving threats.

“It is increasingly important for organizations to assess and manage digital identity
security risks, such as unauthorized access due to impersonation. As organizations
consult these guidelines, they should consider potential impacts to the confidentiality,
integrity, and availability of information and information systems that they manage, and
that their service providers and business partners manage, on behalf of the individuals
and communities that they serve.
Federal agencies implementing these guidelines are required to meet statutory
responsibilities, including those under the Federal Information Security Modernization
Act (FISMA) of 2014 [FISMA] and related NIST standards and guidelines. NIST
recommends that non-federal organizations implementing these guidelines follow
comparable standards (e.g., ISO/IEC 27001) to ensure the secure operation of their
digital systems.”

Download the complete guide HERE

EU AI Act concerning Risk Management Systems for High-Risk AI

Understanding the EU AI Act: A Risk-Based Framework for Trustworthy AI – Implications for U.S. Organizations

Interpretation of Ethical AI Deployment under the EU AI Act

Aligning with ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act

State of Agentic AI Security and Governance

AI Governance: Applying AI Policy and Ethics through Principles and Assessments

ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

Businesses leveraging AI should prepare now for a future of increasing regulation.

Digital Ethics in the Age of AI

DISC InfoSec’s earlier posts on the AI topic

Tags: Digital Identity Security

Comments (1)

Aug 14 2025

Securing Agentic AI: Emerging Risks and Governance Imperatives

Category: AI — disc7 @ 11:43 pm

Agentic AI—systems capable of planning, taking initiative, and pursuing goals with minimal oversight—represents a major shift from traditional, narrow AI tools. This autonomy enables powerful new capabilities but also creates unprecedented security risks. Autonomous agents can adapt in real time, set their own subgoals, and interact with complex systems in ways that are harder to predict, control, or audit.

Key challenges include unpredictable emergent behaviors, coordinated actions in multi-agent environments, and goal misalignment that leads to reward hacking or exploitation of system weaknesses. An agent that seems safe in testing may later bypass security controls, manipulate inputs, or collude with other agents to gain unauthorized access or disrupt operations. These risks are amplified by continuous operation, where small deviations can escalate into severe breaches over time.

Further, agentic systems can autonomously use tools, integrate with third-party services, and even modify their own code—blurring security boundaries. Without strict oversight, these capabilities risk leaking sensitive data, introducing unvetted dependencies, and enabling sophisticated supply chain or privilege escalation attacks. Managing these threats will require new governance, monitoring, and control strategies tailored to the autonomous and adaptive nature of agentic AI.

Agentic AI has the potential to transform industries—from software engineering and healthcare to finance and customer service. However, without robust security measures, these systems could be exploited, behave unpredictably, or trigger cascading failures across both digital and physical environments.

As their capabilities grow, security must be treated as a foundational design principle, not an afterthought—integrated into every stage of development, deployment, and ongoing oversight.

Agentic AI Security

The Agentic AI Bible

Interpretation of Ethical AI Deployment under the EU AI Act

Aligning with ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act

State of Agentic AI Security and Governance

AI Governance: Applying AI Policy and Ethics through Principles and Assessments

ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

Businesses leveraging AI should prepare now for a future of increasing regulation.

Digital Ethics in the Age of AI

DISC InfoSec’s earlier posts on the AI topic

Tags: AI Governance, Securing Agentic AI

Comments (2)

Aug 06 2025

Building Trust with High-Risk AI: What Article 15 of the EU AI Act Means for Accuracy, Robustness & Cybersecurity

Category: AI,Information Security — disc7 @ 4:06 pm

As AI adoption accelerates, especially in regulated or high-impact sectors, the European Union is setting the bar for responsible development. Article 15 of the EU AI Act lays out clear obligations for providers of high-risk AI systems—focusing on accuracy, robustness, and cybersecurity throughout the AI system’s lifecycle. Here’s what that means in practice—and why it matters now more than ever.

1. Security and Reliability From Day One

The AI Act demands that high-risk AI systems be designed with integrity and resilience from the ground up. That means integrating controls for accuracy, robustness, and cybersecurity not only at deployment but throughout the entire lifecycle. It’s a shift from reactive patching to proactive engineering.

2. Accuracy Is a Design Requirement

Gone are the days of vague performance promises. Under Article 15, providers must define and document expected accuracy levels and metrics in the user instructions. This transparency helps users and regulators understand how the system should perform—and flags any deviation from those expectations.

3. Guarding Against Exploitation

AI systems must also be robust against manipulation, whether it’s malicious input, adversarial attacks, or system misuse. This includes protecting against changes to the AI’s behavior, outputs, or performance caused by vulnerabilities or unauthorized interference.

4. Taming Feedback Loops in Learning Systems

Some AI systems continue learning even after deployment. That’s powerful—but dangerous if not governed. Article 15 requires providers to minimize or eliminate harmful feedback loops, which could reinforce bias or lead to performance degradation over time.

5. Compliance Isn’t Optional—It’s Auditable

The Act calls for documented procedures that demonstrate compliance with accuracy, robustness, and security standards. This includes verifying third-party contributions to system development. Providers must be ready to show their work to market surveillance authorities (MSAs) on request.

6. Leverage the Cyber Resilience Act

If your high-risk AI system also falls under the scope of the EU Cyber Resilience Act (CRA), good news: meeting the CRA’s essential cybersecurity requirements can also satisfy the AI Act’s demands. Providers should assess the overlap and streamline their compliance strategies.

7. Don’t Forget the GDPR

When personal data is involved, Article 15 interacts directly with the GDPR—especially Articles 5(1)(d), 5(1)(f), and 32, which address accuracy and security. If your organization is already GDPR-compliant, you’re on the right track, but Article 15 still demands additional technical and operational precision.

Final Thought:

Article 15 raises the bar for how we build, deploy, and monitor high-risk AI systems. It doesn’t just aim to prevent failures—it pushes providers to deliver trustworthy, resilient, and secure AI from the start. For organizations that embrace this proactively, it’s not just about avoiding fines—it’s about building AI systems that earn trust and deliver long-term value.

EU AI Act concerning Risk Management Systems for High-Risk AI

Understanding the EU AI Act: A Risk-Based Framework for Trustworthy AI – Implications for U.S. Organizations

Interpretation of Ethical AI Deployment under the EU AI Act

Aligning with ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act

State of Agentic AI Security and Governance

AI Governance: Applying AI Policy and Ethics through Principles and Assessments

ISO/IEC 42001:2023, First Edition: Information technology – Artificial intelligence – Management system

ISO 42001 Artificial Intelligence Management Systems (AIMS) Implementation Guide: AIMS Framework | AI Security Standards

Businesses leveraging AI should prepare now for a future of increasing regulation.

Digital Ethics in the Age of AI

DISC InfoSec’s earlier posts on the AI topic

Tags: Article 15, EU AI Act

Comments (10)

Aug 06 2025

From Compliance to Confidence: How DISC LLC Delivers Strategic Cybersecurity Services That Scale

Category: Information Security — disc7 @ 1:33 pm

Transforming Cybersecurity & Compliance into Strategic Strength

In an era of ever-tightening regulations and ever-evolving threats, Deura InfoSec Consulting (DISC LLC) stands out by turning compliance from a checkbox into a proactive asset.

🛡️ What We Offer: Core Services at a Glance

1. vCISO Services

Access seasoned CISO-level expertise—without the cost of a full-time executive. Our vCISO services provide strategic leadership, ongoing security guidance, executive reporting, and risk management aligned with your business needs.

2. Compliance & Certification Support

Whether you’re targeting ISO 27001, ISO 27701, ISO 42001, NIST, GDPR, SOC 2, HIPAA, or PCI DSS, DISC supports your entire journey—from assessments and gap analysis to policy creation, control implementation, and audit preparation.

3. Security Risk Assessments

Identify risks across infrastructure, cloud, vendors, and business-critical systems using frameworks such as MITRE ATT&CK (via CALDERA), with actionable risk scorecards and remediation roadmaps.

4. Risk‑based Strategic Planning

We bridge the gap from your current (“as‑is”) security state to your desired (“to‑be”) maturity level. Our process includes strategic roadmapping, metrics to measure progress, and embedding business-aligned security into operations.

5. Security Awareness & Training

Equip your workforce and leadership with tailored training programs—ranging from executive briefings to role-based education—in vital areas like governance, compliance, and emerging threats.

6. Penetration Testing & Tool Oversight

Using top-tier tools like Burp Suite Pro and OWASP ZAP, DISC uncovers vulnerabilities in web applications and APIs. These assessments are accompanied by remediation guidance and optional managed detection support.

7. At DISC LLC, we help organizations harness the power of data and artificial intelligence—responsibly. Our AIMS (Artificial Intelligence Management System) & Data Governance solutions are designed to reduce risk, ensure compliance, and build trust. We implement governance frameworks that align with ISO 27001, ISO 27701, ISO 42001, GDPR, EU AI ACT, HIPAA, and CCPA, supporting both data accuracy and AI accountability. From data classification policies to ethical AI guidelines, bias monitoring, and performance audits, our approach ensures your AI and data strategies are transparent, secure, and future-ready. By integrating AI and data governance, DISC empowers you to lead with confidence in a rapidly evolving digital world.

🔍 Why DISC Works

Fixed-fee, hands‑on approach: No bloated documents, just precise and efficient delivery aligned with your needs.
Expert-led services: With 20+ years in security and compliance, DISC’s consultants guide you at every stage.
Audit-ready processes: Leverage frameworks and tools like GRC platform to streamline compliance, reduce overhead, and stay audit-ready.
Tailored to SMBs & enterprises: From startups to established firms, DISC crafts solutions scalable to your size and skillset.

🚀 Ready to Elevate Your Security?

DISC LLC is more than a service provider—it’s your long-term advisor. Whether you’re combating cyber risk or scaling your compliance posture, our services deliver predictable value and empower you to make security a strategic advantage.

Get started today with a free consultation, including a one-hour session with a vCISO, to see where your organization stands—and where it needs to go.

Info@deurainfosec.com |   https://www.deurainfosec.com | 📞 (707) 998-5164

Expertise-in-Virtual-CISO-vCISO-Services-2 Download

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Comments (10)

Aug 06 2025

State of Agentic AI Security and Governance

Category: AI — disc7 @ 9:28 am

OWASP report “State of Agentic AI Security and Governance v1.0”

Agentic AI: The Future Is Autonomous — and Risky

Agentic AI is no longer a lab experiment—it’s rapidly becoming the foundation of next-gen software, where autonomous agents reason, make decisions, and execute multi-step tasks across APIs and tools. While the economic upside is massive, so is the risk. As OWASP’s State of Agentic AI Security and Governance report highlights, these systems require a complete rethink of security, compliance, and operational control.

1. Agents Are Not Just Smarter—They’re Also Riskier

Unlike traditional AI, Agentic AI systems operate with memory, access privileges, and autonomy. This makes them vulnerable to manipulation: prompt injection, memory poisoning, and abuse of tool integrations. Left unchecked, they can expose sensitive data, trigger unauthorized actions, and bypass conventional monitoring entirely.

2. New Tech, New Threat Surface

Agentic AI introduces risks that traditional security models weren’t built for. Agents can be hijacked or coerced into harmful behavior. Insider threats grow more complex when users exploit agents to perform actions under the radar. With dynamic RAG pipelines and tool calling, a single prompt can become a powerful exploit vector.

3. Frameworks and Protocols Lag Behind

Popular open-source and SaaS frameworks like AutoGen, crewAI, and LangGraph are powerful—but most lack native security features. Protocols like A2A and MCP enable cross-agent communication, but they introduce new vulnerabilities like spoofed identities, data leakage, and action misalignment. Developers are now responsible for stitching together secure systems from pieces that were never designed with security first.

4. A New Compliance Era Has Begun

Static compliance is obsolete. Regulations like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 call for real-time oversight, red-teaming, human-in-the-loop (HITL) controls, and signed audit logs. States like Texas and California are already imposing fines, audit mandates, and legal accountability for autonomous decisions.

5. Insiders Now Have Superpowers

Agents deployed inside organizations often carry privileged access. A malicious insider can abuse that access—exfiltrating data, poisoning RAG sources, or hijacking workflows—all through benign-looking prompts. Worse, most traditional monitoring tools won’t catch these abuses because the agent acts on the user’s behalf.

6. Adaptive Governance Is Now Mandatory

The report calls for adaptive governance models. Think: real-time dashboards, tiered autonomy ladders, automated policy updates, and kill switches. Governance must move at the speed of the agents themselves, embedding ethics, legal constraints, and observability into the code—not bolting them on afterward.

7. Benchmarks and Tools Are Emerging

Security benchmarking is still evolving, but tools like AgentDojo, DoomArena, and Agent-SafetyBench are laying the groundwork. They focus on adversarial robustness, intrinsic safety, and behavior under attack. Expect continuous red-teaming to become as common as pen testing.

8. Self-Governing AI Systems Are the Future

AI agents that evolve and self-learn can’t be governed manually. The report urges organizations to build systems that self-monitor, self-report, and self-correct—all while meeting emerging global standards. Static risk models, annual audits, and post-incident reviews just won’t cut it anymore.

🧠 Final Thought

Agentic AI is here—and it’s powerful, productive, and dangerous if not secured properly. OWASP’s guidance makes it clear: the future belongs to those who embrace proactive security, continuous governance, and adaptive compliance. Whether you’re a developer, CISO, or AI product owner, now is the time to act.

The EU AI Act: Answers to Frequently Asked Questions

EU AI ACT 2024

EU publishes General-Purpose AI Code of Practice: Compliance Obligations Begin August 2025

ISO 42001 Readiness: A 10-Step Guide to Responsible AI Governance

Aligning with ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act

The Strategic Synergy: ISO 27001 and ISO 42001 – A New Era in Governance

Clause 4 of ISO 42001: Understanding an Organization and Its Context and Why It Is Crucial to Get It Right.

Think Before You Share: The Hidden Privacy Costs of AI Convenience

The AI Readiness Gap: High Usage, Low Security

Mitigate and adapt with AICM (AI Controls Matrix)

DISC InfoSec’s earlier posts on the AI topic

Secure Your Business. Simplify Compliance. Gain Peace of Mind

Tags: Agentic AI Governance, Agentic AI Security

Comments (0)

Aug 06 2025

IBM’s Five-Pillar Framework for Securing Generative AI: A Lifecycle-Based Approach to Risk Management

Category: AI — disc7 @ 7:39 am

IBM introduces a structured approach to securing generative AI by focusing on protection at each phase of the AI lifecycle. The framework emphasizes securing three critical elements: the data consumed by AI systems, the model itself (during development/training), and the usage environment (live inference). These are supported by robust infrastructure controls and governance mechanisms to oversee fairness, bias, and drift over time.

In the data collection and handling stage, risks include centralized repositories that grant broad access to intellectual property and personally identifiable information (PII). To mitigate threats like data exfiltration or misuse, IBM recommends rigorous access controls, encryption, and continuous risk assessments tailored to specific data types.

Next, during model development and training, the framework warns about threats such as data poisoning and the insertion of malicious code. It advises implementing secure development practices—scanning for vulnerabilities, enforcing access policies, and treating the model build process with the same rigor as secure software development.

When it comes to model inference and live deployment, organizations face risks like prompt‑injection, adversarial attacks, and unauthorized usage. IBM recommends real-time monitoring, anomaly detection, usage policies, and safeguards to validate inputs and outputs in live AI environments.

Beyond securing each phase of the pipeline, the framework emphasizes the importance of securing the underlying infrastructure—infrastructure-as-a-service, compute nodes, storage systems—so that large language models and associated applications operate in hardened, compliant environments.

Crucially, IBM insists on embedding strong AI governance: policies, oversight structures, and continuous monitoring to detect bias, drift, and compliance issues. Governance should integrate with existing regulatory frameworks like the NIST AI Risk Management Framework and adapt alongside evolving regulations such as the EU AI Act.

Additionally, IBM’s broader work—including partnerships with AWS and internal tools like X‑Force Red—surfaced common gaps in security posture: many organizations prioritize innovation over security. Findings indicate that most active generative AI initiatives lack foundational controls across these five pillars: data, model, usage, infrastructure, and governance.

Opinion

IBM’s framework delivers a well-structured, holistic approach to the complex challenge of securing generative AI. By breaking security into discrete but interlinked phases — data, model, usage, infrastructure, governance — it helps organizations methodically build defenses where vulnerabilities are most likely. It’s also valuable that IBM aligns its framework with broader models such as NIST and incorporates continuous governance, which is essential in fast-moving AI environments.

That said, the real test lies in execution. Many enterprises still grapple with “shadow AI” — unsanctioned AI tools used by employees — and IBM’s own recent breach report suggests that only around 3% of organizations studied have adequate AI access controls in place, despite steep average breach costs ($670K extra from shadow AI alone). This gap between framework and reality underscores the need for cultural buy-in, investment in tooling, and staff training alongside technical controls.

All told, IBM’s Framework for Securing Generative AI is a strong starting point—especially when paired with governance, red teaming, infrastructure hardening, and awareness programs. But its impact will vary widely depending on how well organizations integrate its principles into everyday operations and security culture.