DISC InfoSec blog

Jan 05 2026

Why IRT War Rooms Are Critical for Effective Incident Response

Category: Security Incident — disc7 @ 12:22 pm

IRT war rooms are essential because they impose discipline, focus, and clarity at the exact moment when chaos is most likely to take over.

During serious incidents, the biggest risks are not just technical failures but fragmented communication, unclear ownership, and cognitive overload. A war room creates a single source of truth—one place where decisions are made, actions are tracked, and priorities are aligned. This dramatically reduces duplication of effort, conflicting instructions, and rumor-driven responses.

War rooms also enforce accountability under pressure. By clearly assigning roles (via RACI), verbalizing milestones, and recording decisions, they prevent hindsight confusion and “who knew what, when” disputes. This is invaluable not only for recovery, but also for executive briefings, legal defensibility, and regulatory scrutiny.

Equally important, war rooms protect the response team. By isolating responders from constant interruptions and external noise, they preserve cognitive bandwidth—something that is often underestimated but critical in high-severity incidents where small mistakes can have outsized consequences.

In short, an effective IRT war room turns incident response from a reactive scramble into a controlled, auditable, and business-aligned operation. Organizations that treat war rooms as a formal capability—rather than an ad hoc call—consistently respond faster, communicate better, and recover with less damage.

When a security incident escalates to Severity Level 2 or higher, establishing an Incident Response (IRT) war room becomes critical. A war room allows responders to step away from daily distractions, maintain focus, and work in a tightly coordinated environment. By isolating the response team, organizations reduce noise, prevent miscommunication, and enable faster, more accurate decision-making during high-pressure situations.

The war room is initiated by the incident lead and typically takes the form of a dedicated Zoom session that remains open throughout the active phase of the incident. Recording the session ensures that decisions, discussions, and actions are fully captured. Early in the meeting, a designated reporter is assigned to provide structured and periodic updates to key stakeholders who are not directly involved in the response, ensuring transparency without disrupting the response team.

Clear roles and accountability are essential in a war room. The team should reference the IRT RACI chart to announce major response functions and confirm ownership of each activity. Key milestones—such as completing a preliminary damage assessment—should be explicitly stated and shared as they occur. This structured approach ensures leadership and external stakeholders receive consistent, accurate updates aligned with the incident’s progression.

As response activities unfold, actions taken by the team should be clearly described and documented during the session. Capturing sufficient detail in real time helps preserve institutional knowledge and creates a reliable record of how the incident was handled. The communication method used should align with the severity level, ensuring the right balance between speed, accuracy, and control.

Once the Zoom recording is available, a transcript is generated and stored along with the recording in the organization’s IRT repository. The transcript is also uploaded to the document management system, where summaries can be produced for post-incident analysis, reporting, and continuous improvement. In my view, well-run IRT war rooms are not just operational tools—they are critical governance mechanisms that improve response quality, accountability, and long-term security maturity.

At DISC InfoSec, we help organizations navigate this landscape by aligning AI risk management, governance, security, and compliance into a single, practical roadmap. Whether you are experimenting with AI or deploying it at scale, we help you choose and operationalize the right frameworks to reduce risk and build trust. Learn more at DISC InfoSec.

Tags: Incident war rooms, IRT war rooms

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Jan 05 2026

Deepfakes Cost $25 Million: Why Old-School Verification Still Works

Category: AI,AI Governance,Deepfakes — disc7 @ 9:01 am

A British engineering firm reportedly lost $25 million after an employee joined a video call that appeared to include their CFO. The voice, the face, and the mannerisms all checked out—but it wasn’t actually him. The incident highlights how convincing deepfake technology has become and how easily trust can be exploited.

This case shows that visual and audio cues alone are no longer reliable for verification. AI can now replicate voices and faces with alarming accuracy, making traditional “it looks and sounds right” judgment calls dangerously insufficient, especially under pressure.

Ironically, the most effective countermeasure to advanced AI attacks isn’t more technology—it’s simpler, human-centered controls. When digital signals can be forged, analog verification methods regain their value.

One such method is establishing a “safe word.” This is a randomly chosen word known only to a small, trusted group and never shared via email, chat, or documents. It lives only in human memory.

If an urgent request comes in—whether from a “CEO,” “CFO,” or even a family member—especially involving money or sensitive actions, the response should be to pause and ask for the safe word. An AI can mimic a voice, but it cannot reliably guess a secret it was never trained on.

My opinion: Safe words may sound old-fashioned, but they are practical, low-cost, and highly effective in a world of deepfakes and social engineering. Every finance team—and even families—should treat this as a basic risk control, not a gimmick. In high-risk moments, simple friction can be the difference between trust and a multimillion-dollar loss.

#CyberSecurity #DeepFakes #SocialEngineering #AI #RiskManagement

Tags: Deepfake, Deepfakes and Fraud

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Jan 04 2026

AI Governance That Actually Works: Beyond Policies and Promises

Category: AI,AI Governance,AI Guardrails,ISO 42001,NIST CSF — disc7 @ 3:33 pm

1. AI Has Become Core Infrastructure
AI is no longer experimental — it’s now deeply integrated into business decisions and societal functions. With this shift, governance can’t stay theoretical; it must be operational and enforceable. The article argues that combining the NIST AI Risk Management Framework (AI RMF) with ISO/IEC 42001 makes this operationalization practical and auditable.

2. Principles Alone Don’t Govern
The NIST AI RMF starts with the Govern function, stressing accountability, transparency, and trustworthy AI. But policies by themselves — statements of intent — don’t ensure responsible execution. ISO 42001 provides the management-system structure that anchors these governance principles into repeatable business processes.

3. Mapping Risk in Context
Understanding the context and purpose of an AI system is where risk truly begins. The NIST RMF’s Map function asks organizations to document who uses a system, how it might be misused, and potential impacts. ISO 42001 operationalizes this through explicit impact assessments and scope definitions that force organizations to answer difficult questions early.

4. Measuring Trust Beyond Accuracy
Traditional AI metrics like accuracy or speed fail to capture trustworthiness. The NIST RMF expands measurement to include fairness, explainability, privacy, and resilience. ISO 42001 ensures these broader measures aren’t aspirational — they require documented testing, verification, and ongoing evaluation.

5. Managing the Full Lifecycle
The Manage function addresses what many frameworks ignore: what happens after AI deployment. ISO 42001 formalizes post-deployment monitoring, incident reporting and recovery, decommissioning, change management, and continuous improvement — framing AI systems as ongoing risk assets rather than one-off projects.

6. Third-Party & Supply Chain Risk
Modern AI systems often rely on external data, models, or services. Both frameworks treat third-party and supplier risks explicitly — a critical improvement, since risks extend beyond what an organization builds in-house. This reflects growing industry recognition of supply chain and ecosystem risk in AI.

7. Human Oversight as a System
Rather than treating human review as a checkbox, the article emphasizes formalizing human roles and responsibilities. It calls for defined escalation and override processes, competency-based training, and interdisciplinary decision teams — making oversight deliberate, not incidental.

8. Strategic Value of NIST-ISO Alignment
The real value isn’t just technical alignment — it’s strategic: helping boards, executives, and regulators speak a common language about risk, accountability, and controls. This positions organizations to be both compliant with emerging regulations and competitive in markets where trust matters.

9. Trust Over Speed
The article closes with a cultural message: in the next phase of AI adoption, trust will outperform speed. Organizations that operationalize responsibility (through structured frameworks like NIST AI RMF and ISO 42001) will lead, while those that chase innovation without governance risk reputational harm.

10. Practical Implications for Leaders
For AI leaders, the takeaway is clear: you need both risk-management logic and a management system to ensure accountability, measurement, and continuous improvement. Cryptic policies aren’t enough; frameworks must translate into auditable, executive-reportable actions.

Opinion

This article provides a thoughtful and practical bridge between high-level risk principles and real-world governance. NIST’s AI RMF on its own captures what needs to be considered (governance, context, measurement, and management) — a critical starting point for responsible AI risk management. (NIST)

But in many organizations today, abstract frameworks don’t translate into disciplined execution — that gap is exactly where ISO/IEC 42001 can add value by prescribing systematic processes, roles, and continuous improvement cycles. Together, the NIST AI RMF and ISO 42001 form a stronger operational baseline for responsible, auditable AI governance.

In practice, however, the challenge will be in integration — aligning governance systems already in place (e.g., ISO 27001, internal risk programs) with these newer AI standards without creating redundancy or compliance fatigue. The real test of success will be whether organizations can bake these practices into everyday decision-making, not just compliance checklists.

Tags: ISO 42001, NIST AI Risk Management Framework, NIST AI RMF

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Jan 03 2026

Choosing the Right AI Security Frameworks: A Practical Roadmap for Secure AI Adoption

Category: AI,AI Governance,ISO 42001,Security and privacy Law,Security Compliance — disc7 @ 11:22 pm

Choosing the right AI security framework is becoming a critical decision as organizations adopt AI at scale. No single framework solves every problem. Each one addresses a different aspect of AI risk, governance, security, or compliance, and understanding their strengths helps organizations apply them effectively.

The NIST AI Risk Management Framework (AI RMF) is best suited for managing AI risks across the entire lifecycle—from design and development to deployment and ongoing use. It emphasizes trustworthy AI by addressing security, privacy, safety, reliability, and bias. This framework is especially valuable for organizations that are building or rapidly scaling AI capabilities and need a structured way to identify and manage AI-related risks.

ISO/IEC 42001, the AI Management System (AIMS) standard, focuses on governance rather than technical controls. It helps organizations establish policies, accountability, oversight, and continuous improvement for AI systems. This framework is ideal for enterprises deploying AI across multiple teams or business units and looking to formalize AI governance in a consistent, auditable way.

For teams building AI-enabled applications, the OWASP Top 10 for LLMs and Generative AI provides practical, hands-on security guidance. It highlights common and emerging risks such as prompt injection, data leakage, insecure output handling, and model abuse. This framework is particularly useful for AppSec and DevSecOps teams securing AI interfaces, APIs, and user-facing AI features.

MITRE ATLAS takes a threat-centric approach by mapping adversarial tactics and techniques that target AI systems. It is well suited for threat modeling, red-team exercises, and AI breach simulations. By helping security teams think like attackers, MITRE ATLAS strengthens defensive strategies against real-world AI threats.

From a regulatory perspective, the EU AI Act introduces a risk-based compliance framework for organizations operating in or offering AI services within the European Union. It defines obligations for high-risk AI systems and places strong emphasis on transparency, accountability, and risk controls. For global organizations, this regulation is becoming a key driver of AI compliance strategy.

The most effective approach is not choosing one framework, but combining them. Using NIST AI RMF for risk management, ISO/IEC 42001 for governance, OWASP and MITRE for technical security, and the EU AI Act for regulatory compliance creates a balanced and defensible AI security posture.

Tags: AI Security Frameworks

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Jan 03 2026

Self-Assessment Tools That Turn Compliance Confusion into a Clear Roadmap

Category: AI Governance Tools,CISO,GRC,ISO 27k,ISO 42001,Security Tools,vCISO — disc7 @ 11:04 pm

GRC Solutions offers a collection of self-assessment and gap analysis tools designed to help organisations evaluate their current compliance and risk posture across a variety of standards and regulations. These tools let you measure how well your existing policies, controls, and processes match expectations before you start a full compliance project.
Several tools focus on ISO standards, such as ISO 27001:2022 and ISO 27002 (information security controls), which help you identify where your security management system aligns or falls short of the standard’s requirements. Similar gap analysis tools are available for ISO 27701 (privacy information management) and ISO 9001 (quality management).
For data protection and privacy, there are GDPR-related assessment tools to gauge readiness against the EU General Data Protection Regulation. These help you see where your data handling and privacy measures require improvement or documentation before progressing with compliance work.
The Cyber Essentials Gap Analysis Tool is geared toward organisations preparing for this basic but influential UK cybersecurity certification. It offers a simple way to assess the maturity of your cyber controls relative to the Cyber Essentials criteria.
Tools also cover specialised areas such as PCI DSS (Payment Card Industry Data Security Standard), including a self-assessment questionnaire tool to help identify how your card-payment practices align with PCI requirements.
There are industry-specific and sector-tailored assessment tools too, such as versions of the GDPR gap assessment tailored for legal sector organisations and schools, recognising that different environments have different compliance nuances.
Broader compliance topics like the EU Cloud Code of Conduct and UK privacy regulations (e.g., PECR) are supported with gap assessment or self-assessment tools. These allow you to review relevant controls and practices in line with the respective frameworks.
A NIST Gap Assessment Tool helps organisations benchmark against the National Institute of Standards and Technology framework, while a DORA Gap Analysis Tool addresses preparedness for digital operational resilience regulations impacting financial institutions.
Beyond regulatory compliance, the catalogue includes items like a Business Continuity Risk Management Pack and standards-related gap tools (e.g., BS 31111), offering flexibility for organisations to diagnose gaps in broader risk and continuity planning areas as well.

Self-assessment tools

Browse wide range of self-assessment tools, covering topics such as the GDPR, ISO 27001 and Cyber Essentials, to identify the gaps in your compliance projects.

Tags: Self Assessment Tools

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Jan 03 2026

8 Practical Cybersecurity Steps Every Small Business Can Take Today

Category: cyber security,Information Security — disc7 @ 11:47 am

Many small and medium businesses are attractive targets for cybercriminals because they hold valuable data and often lack extensive IT resources. Threats like ransomware, phishing and business email compromise can disrupt operations, damage reputation, and cause financial loss. Recognizing that no business is too small to be targeted is the first step toward protection.

1. Teach employees to recognize and report phishing attacks. Phishing is one of the primary ways attackers gain access. Regular awareness training helps staff spot suspicious emails, links, and requests, reducing the chance that a click triggers a breach.

2. Require strong passwords across your organization. Weak or reused passwords are easily guessed or brute-forced. Establish a strong password policy and consider tools like password managers so employees can securely store unique credentials.

3. Implement multifactor authentication (MFA). Adding MFA means users must provide more than just a password to access accounts. This extra layer of verification dramatically reduces the odds that attackers can impersonate employees, even if they obtain a password.

4. Keep systems and software up to date. Outdated software often contains known security flaws that attackers exploit. Having regular patching schedules and enabling automatic updates wherever possible keeps your systems protected against many common vulnerabilities.

5. Enable logging and monitoring. Logging system activity gives you visibility into what’s happening on your network. Monitoring logs helps detect suspicious behavior early, so you can respond before an incident becomes a major breach.

6. Back up your business data regularly. Ransomware and other failures can cripple operations if you can’t access critical files. Maintain backups following a reliable strategy—such as the 3-2-1 rule—to ensure you can restore data quickly and resume business functions.

7. Encrypt sensitive data and devices. Encryption transforms your data into unreadable code for anyone without access keys. Applying encryption to data at rest and in transit helps protect information even if a device is lost or a system is compromised.

8. Report cyber incidents and share threat information. If an incident occurs, reporting it to agencies like CISA helps the broader business community stay informed about emerging threats and may provide access to additional guidance or alerts.

Taken together, these steps create a practical cybersecurity foundation for your business. Start with basics like employee training and MFA, then build up to backups, encryption, and incident reporting to strengthen your resiliency against evolving threats.

Source: You Can Protect Your Business from Online Threats (CISA)

Tags: Cybersecurity for SMBs

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Jan 02 2026

No Breach, No Alerts—Still Stolen: When AI Models Are Taken Without Being Hacked

Category: AI,AI Governance,AI Guardrails — disc7 @ 11:11 am

No Breach. No Alerts. Still Stolen: The Model Extraction Problem

1. A company can lose its most valuable AI intellectual property without suffering a traditional security breach. No malware, no compromised credentials, no incident tickets—just normal-looking API traffic. Everything appears healthy on dashboards, yet the core asset is quietly walking out the door.

2. This threat is known as model extraction. It happens when an attacker repeatedly queries an AI model through legitimate interfaces—APIs, chatbots, or inference endpoints—and learns from the responses. Over time, they can reconstruct or closely approximate the proprietary model’s behavior without ever stealing weights or source code.

3. A useful analogy is a black-box expert. If I can repeatedly ask an expert questions and carefully observe their answers, patterns start to emerge—how they reason, where they hesitate, and how they respond to edge cases. Over time, I can train someone else to answer the same questions in nearly the same way, without ever seeing the expert’s notes or thought process.

4. Attackers pursue model extraction for several reasons. They may want to clone the model outright, steal high-value capabilities, distill it into a cheaper version using your model as a “teacher,” or infer sensitive traits about the training data. None of these require breaking in—only sustained access.

5. This is why AI theft doesn’t look like hacking. Your model can be copied simply by being used. The very openness that enables adoption and revenue also creates a high-bandwidth oracle for adversaries who know how to exploit it.

6. The consequences are fundamentally business risks. Competitive advantage evaporates as others avoid your training costs. Attackers discover and weaponize edge cases. Malicious clones can damage your brand, and your IP strategy collapses because the model’s behavior has effectively been given away.

7. The aftermath is especially dangerous because it’s invisible. There’s no breach report or emergency call—just a competitor releasing something “surprisingly similar” months later. By the time leadership notices, the damage is already done.

8. At scale, querying equals learning. With enough inputs and outputs, an attacker can build a surrogate model that is “good enough” to compete, abuse users, or undermine trust. This is IP theft disguised as legitimate usage.

9. Defending against this doesn’t require magic, but it does require intent. Organizations need visibility by treating model queries as security telemetry, friction by rate-limiting based on risk rather than cost alone, and proof by watermarking outputs so stolen behavior can be attributed when clones appear.

My opinion: Model extraction is one of the most underappreciated risks in AI today because it sits at the intersection of security, IP, and business strategy. If your AI roadmap focuses only on performance, cost, and availability—while ignoring how easily behavior can be copied—you don’t really have an AI strategy. Training models is expensive; extracting behavior through APIs is cheap. And in most markets, “good enough” beats “perfect.”

Tags: AI Models, Hacked

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Jan 01 2026

Not All Risks Are Equal: What Every Organization Must Know

Category: Risk Assessment,Security Risk Assessment — disc7 @ 11:15 am

Types of Risk & Risk Assessment

Organizations face multiple types of risks that can affect strategy, operations, compliance, and reputation. Strategic risks arise when business objectives or long-term goals are threatened—such as when weak security planning damages customer confidence. Operational risks stem from human errors, flawed processes, or technology failures, like a misconfigured firewall or inadequate incident response.

Cyber and information security risks affect the confidentiality, integrity, and availability of data. Examples include ransomware attacks, data breaches, and insider threats. Compliance or regulatory risks occur when companies fail to meet legal or industry requirements such as ISO 27001, ISO 42001, GDPR, PCI-DSS, or IEC standards.

Financial risk is tied to monetary losses through fraud, fines, or system downtime. Reputational risks damage stakeholder trust and the public perception of the organization, often triggered by events like public breach disclosures. Lastly, third-party/vendor risks originate from suppliers and partners, such as when a vendor’s weak cybersecurity exposes the organization.

Risk assessment is the structured process used to protect the business from these threats, ensuring vulnerabilities are addressed before causing harm. The assessment lifecycle involves five key phases:
1️⃣ Identifying risks through understanding assets and their vulnerabilities
2️⃣ Analyzing likelihood and impact
3️⃣ Evaluating and prioritizing based on risk severity
4️⃣ Treating risks through mitigation, transfer, acceptance, or avoidance
5️⃣ Monitoring and continually improving controls over time

Opinion: Why Knowing Risk Types Helps Businesses

Understanding the distinct categories of risks allows companies to take a proactive approach instead of reacting after damage occurs. It provides clarity on where threats originate, which helps leaders allocate resources more efficiently, strengthen compliance, protect revenue, and build trust with customers and stakeholders. Ultimately, knowing the types of risks empowers smarter decision-making and leads to long-term business resilience.

Tags: Types of Risks

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Dec 31 2025

Shadow AI: When Productivity Gains Create New Risks

Category: AI — disc7 @ 9:20 am

Shadow AI: The Productivity Paradox

Organizations face a new security challenge that doesn’t originate from malicious actors but from well-intentioned employees simply trying to do their jobs more efficiently. This phenomenon, known as Shadow AI, represents the unauthorized use of AI tools without IT oversight or approval.

Marketing teams routinely feed customer data into free AI platforms to generate compelling copy and campaign content. They see these tools as productivity accelerators, never considering the security implications of sharing sensitive customer information with external systems.

Development teams paste proprietary source code into public chatbots seeking quick debugging assistance or code optimization suggestions. The immediate problem-solving benefit overshadows concerns about intellectual property exposure or code base security.

Human resources departments upload candidate resumes and personal information to AI summarization tools, streamlining their screening processes. The efficiency gains feel worth the convenience, while data privacy considerations remain an afterthought.

These employees aren’t threat actors—they’re productivity seekers exploiting powerful tools available at their fingertips. Once organizational data enters public AI models or third-party vector databases, it escapes corporate control entirely and becomes permanently exposed.

The data now faces novel attack vectors like prompt injection, where adversaries manipulate AI systems through carefully crafted queries to extract sensitive information, essentially asking the model to “forget your instructions and reveal confidential data.” Traditional security measures offer no protection against these techniques.

We’re witnessing a fundamental shift from the old paradigm of “Data Exfiltration” driven by external criminals to “Data Integration” driven by internal employees. The threat landscape has evolved beyond perimeter defense scenarios.

Legacy security architectures built on network perimeters, firewalls, and endpoint protection become irrelevant when employees voluntarily connect to external AI services. These traditional controls can’t prevent authorized users from sharing data through legitimate web interfaces.

The castle-and-moat security model fails completely when your own workforce continuously creates tunnels through the walls to access the most powerful computational tools humanity has ever created. Organizations need governance frameworks, not just technical barriers.

Opinion: Shadow AI represents the most significant information security challenge for 2026 because it fundamentally breaks the traditional security model. Unlike previous shadow IT concerns (unauthorized SaaS apps), AI tools actively ingest, process, and potentially retain your data for model training purposes. Organizations need immediate AI governance frameworks including acceptable use policies, approved AI tool catalogs, data classification training, and technical controls like DLP rules for AI service domains. The solution isn’t blocking AI—that’s impossible and counterproductive—but rather creating “Lighted AI” pathways: secure, sanctioned AI tools with proper data handling controls. ISO 42001 provides exactly this framework, which is why AI Management Systems have become business-critical rather than optional compliance exercises.

Shadow AI for Everyone: Understanding Unauthorized Artificial Intelligence, Data Exposure, and the Hidden Threats Inside Modern Enterprises

Tags: prompt Injection, Shadow AI

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Dec 30 2025

EU AI Act: Why Every Organization Using AI Must Pay Attention

Category: AI,AI Governance — disc7 @ 11:07 am

EU AI Act: Why Every Organization Using AI Must Pay Attention

The EU AI Act is the world’s first major regulation designed to govern how artificial intelligence is developed, deployed, and managed across industries. Approved in June 2024, it establishes harmonized rules for AI use across all EU member states — just as GDPR did for privacy.

Any organization that builds, integrates, or sells AI systems within the European Union must comply — even if they are headquartered outside the EU. That means U.S. and global companies using AI in European markets are officially in scope.

The Act introduces a risk-based regulatory model. AI is categorized across four risk tiers — from unacceptable, which are completely banned, to high-risk, which carry strict controls, limited-risk with transparency requirements, and minimal-risk, which remain largely unregulated.

High-risk AI includes systems governing access to healthcare, finance, employment, critical infrastructure, law enforcement, and essential public services. Providers of these systems must implement rigorous risk management, governance, monitoring, and documentation processes across the entire lifecycle.

Certain AI uses are explicitly prohibited — such as social scoring, biometric emotion recognition in workplaces or schools, manipulative AI techniques, and untargeted scraping of facial images for surveillance.

Compliance obligations are rolling out in phases beginning February 2025, with core high-risk system requirements taking effect in August 2026 and final provisions extending through 2027. Organizations have limited time to assess their current systems and prepare for adherence.

This legislation is expected to shape global AI governance frameworks — much like GDPR influenced worldwide privacy laws. Companies that act early gain an advantage: reduced legal exposure, customer trust, and stronger market positioning.

How DISC InfoSec Helps You Stay Ahead

DISC InfoSec brings 20+ years of security and compliance excellence with a proven multi-framework approach. Whether preparing for EU AI Act, ISO 42001, GDPR, SOC 2, or enterprise governance — we help organizations implement responsible AI controls without slowing innovation.

If your business touches the EU and uses AI — now is the time to get compliant.

📩 Let’s build your AI governance roadmap together.
Reach out: Info@DeuraInfosec.com

Earlier posts covering the EU AI Act

How ISO 42001 Strengthens Alignment With the EU AI Act (Without Replacing Legal Compliance)

Understanding Your AI System’s Risk Level: A Guide to EU AI Act Compliance

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

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

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

Tags: EU AI Act

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Dec 30 2025

From Regulation to Revenue: The Power of Strong Security Compliance

Category: Information Security — disc7 @ 8:15 am

Compliance today isn’t just about checking boxes — it’s directly tied to business survival and stakeholder trust.

Organizations now face intense scrutiny from clients, regulators, and supply chain partners. With reputations and revenue on the line, getting compliance right the first time is essential.

DISC InfoSec has been leading that mission since 2002, supporting businesses across industries in achieving and sustaining certification.

Our team includes seasoned specialists with over 20 years of practical experience in security and compliance.

We specialize in multi-framework strategies — including ISO 27001, ISO 42001, GDPR, SOC 2, PCI, and HIPAA — allowing companies to streamline efforts and reduce operational costs.

AI is rapidly reshaping how organizations operate—but without strong oversight, it introduces serious regulatory, ethical, and operational challenges.

ISO 42001 delivers a structured governance framework to ensure AI is developed and used responsibly. It focuses on key safeguards such as bias mitigation, transparency, accountability, and ongoing performance monitoring—especially vital for high-risk sectors like defense, healthcare, and finance.

100% Certification Success: Why Businesses Trust DISC InfoSec

This approach is why we have a 100% client certification success rate with zero exceptions. Every organization we support passes.

From global enterprises to early-stage innovators, we help build security programs that protect contracts, strengthen customer confidence, and ultimately fuel business growth.

When the stakes are high and compliance is mission-critical, you deserve a partner who delivers results — every time.

Partner with DISC InfoSec to secure your compliance roadmap and safeguard your business advantage.
📩 Contact: Info@DeuraInfoSec.com
🔐 www.DeuraInfoSec.com

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Dec 29 2025

12 Pillars of Cybersecurity

Category: cyber security,Information Security — disc7 @ 9:56 am

12 Pillars of Cybersecurity — Simplified Overview — Start by getting the basics right — it’s the foundation of every effective security program.

1️⃣ Disaster Recovery
Disaster Recovery ensures organizations can quickly restore systems and data after a disruptive event such as ransomware, hardware failure, or natural disasters. A well-designed plan includes data backups, documented recovery procedures, and resilience testing so the business can continue operating with minimal downtime.

2️⃣ Authentication
Authentication verifies that users are who they claim to be. Strong password policies, secure login controls, and multifactor authentication (MFA) help prevent unauthorized access to critical systems, reducing the risk of credential theft and account compromise.

3️⃣ Authorization
Authorization determines what authenticated users are allowed to do. Properly managed access roles and least-privilege principles ensure individuals only access the information needed for their job, minimizing internal misuse and breach exposure.

4️⃣ Encryption
Encryption protects sensitive data by making it unreadable to unauthorized entities. Whether data is stored or in transit, encryption standards like TLS help maintain confidentiality and integrity, even if attackers intercept it.

5️⃣ Vulnerability Management
This includes identifying weaknesses in applications, systems, or configurations before attackers exploit them. Regular scanning, patching, and proactive remediation are essential to stay ahead of constantly emerging threats.

6️⃣ Audit & Compliance
Audit and compliance confirm that cybersecurity controls meet legal, industry, and internal requirements. Through continuous monitoring, reporting, and assessments, organizations strengthen governance and reduce regulatory risk.

7️⃣ Network Security
Network security protects communication flowing between devices and systems. Firewalls, intrusion detection, segmentation, and DNS security reduce unauthorized access and lateral movement inside the network.

8️⃣ Terminal (Endpoint) Security
Endpoints—like laptops, servers, and mobile devices—must be protected from malware and misuse. Tools such as EDR (Endpoint Detection & Response), encryption, and device control help secure data where employees work every day.

9️⃣ Emergency Response
Incident Response and business continuity actions are triggered when a cyberattack occurs. Quick detection, containment, and communication limit damage and accelerate recovery while maintaining stakeholder trust.

🔟 Container Security
Containerized workloads, used heavily in cloud environments, require specialized protections. Securing container images, runtime behavior, and orchestration platforms prevents vulnerabilities from spreading rapidly across applications.

1️⃣1️⃣ API Security
APIs are now core to digital integrations, making them a prime target for attackers. Secure authentication, encryption, rate limiting, and runtime monitoring protect data shared between systems and prevent unauthorized access.

1️⃣2️⃣ Third-Party / Vendor Management
Vendors introduce additional risk since their systems may connect to yours. Risk assessments, clear security expectations, and continuous monitoring help ensure third-party access doesn’t become the weakest link.

⭐ Expert Opinion

These 12 pillars offer a strong foundational framework — but cybersecurity only works when measurements, monitoring, and automation continuously improve these controls. With attackers advancing faster every year, organizations must treat cybersecurity as an adaptable lifecycle, not a one-time checklist. Prioritized risk-based implementation and skilled oversight remain the keys to real cyber resilience.

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Dec 26 2025

Are 43-Minute AI Interviews the Future of Hiring — or a Serious Step Back?

Category: AI — disc7 @ 4:59 pm

Many companies are now replacing real human recruiters with lengthy, proctored AI interviews — sometimes lasting over 40 minutes. For candidates, this feels absurd, especially when the system claims to learn about your personality and skills solely through automated prompts and video analysis.

This shift suggests a wider trust in AI for critical hiring decisions, even though cybersecurity failures continue to rise year after year. There’s a growing disconnect between technological adoption and real-world risk management.

Despite the questionable outcomes, companies promote these systems as part of a prestigious hiring process. They boast about connecting top talent with major Silicon Valley firms, projecting confidence that AI-driven evaluation is the future.

Applicants are told that completing a personal AI interview is the next mandatory step to “showcase their skills.” It’s framed as an opportunity rather than another automated filter.

The interview process is positioned as simple and straightforward — roughly 30 minutes, unless you’re applying for an engineering role where a coding challenge is added. No preparation is supposedly required.

A short instructional video is provided so candidates know how the AI interview will operate and what the interface will look like. The message suggests this is for candidate comfort and transparency.

After completing the AI interview, applicants will update their digital profile with any missing information. This profile becomes their automated representation to hiring managers.

Finally, once “certified” by the system, candidates will passively receive interview requests from companies — assuming they meet the algorithm’s standards.

My Opinion

While automation can improve efficiency, replacing real human judgment with AI in the earliest — and most personal — stage of hiring risks turning candidates into data points rather than people. It raises concerns about fairness, privacy, and bias, not to mention the irony that organizations deploying these tools still struggle to secure their own systems. A balance is needed: let AI assist the process, but don’t let it remove humanity from hiring.

Tags: AI Interview

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Dec 26 2025

Why AI-Driven Cybersecurity Frameworks Are Now a Business Imperative

Category: AI,AI Governance,ISO 27k,ISO 42001,NIST CSF,owasp — disc7 @ 8:52 am

A reliable industry context about AI and cybersecurity frameworks from recent market and trend reports. I’ll then give a clear opinion at the end.

1. AI Is Now Core to Cyber Defense
Artificial Intelligence is transforming how organizations defend against digital threats. Traditional signature-based security tools struggle to keep up with modern attacks, so companies are using AI—especially machine learning and behavioral analytics—to detect anomalies, predict risks, and automate responses in real time. This integration is now central to mature cybersecurity programs.

2. Market Expansion Reflects Strategic Adoption
The AI cybersecurity market is growing rapidly, with estimates projecting expansion from tens of billions today into the hundreds of billions within the next decade. This reflects more than hype—organizations across sectors are investing heavily in AI-enabled threat platforms to improve detection, reduce manual workload, and respond faster to attacks.

3. AI Architectures Span Detection to Response
Modern frameworks incorporate diverse AI technologies such as natural language processing, neural networks, predictive analytics, and robotic process automation. These tools support everything from network monitoring and endpoint protection to identity-based threat management and automated incident response.

4. Cloud and Hybrid Environments Drive Adoption
Cloud migrations and hybrid IT architectures have expanded attack surfaces, prompting more use of AI solutions that can scale across distributed environments. Cloud-native AI tools enable continuous monitoring and adaptive defenses that are harder to achieve with legacy on-premises systems.

5. Regulatory and Compliance Imperatives Are Growing
As digital transformation proceeds, regulatory expectations are rising too. Many frameworks now embed explainable AI and compliance-friendly models that help organizations demonstrate legal and ethical governance in areas like data privacy and secure AI operations.

6. Integration Challenges Remain
Despite the advantages, adopting AI frameworks isn’t plug-and-play. Organizations face hurdles including high implementation cost, lack of skilled AI security talent, and difficulties integrating new tools with legacy architectures. These challenges can slow deployment and reduce immediate ROI. (Inferred from general market trends)

7. Sophisticated Threats Demand Sophisticated Defenses
AI is both a defensive tool and a capability leveraged by attackers. Adversarial AI can generate more convincing phishing, exploit model weaknesses, and automate aspects of attacks. A robust cybersecurity framework must account for this dual role and include AI-specific risk controls.

8. Organizational Adoption Varies Widely
Enterprise adoption is strong, especially in regulated sectors like finance, healthcare, and government, while many small and medium businesses remain cautious due to cost and trust issues. This uneven adoption means frameworks must be flexible enough to suit different maturity levels. (From broader industry reports)

9. Frameworks Are Evolving With the Threat Landscape
Rather than static checklists, AI cybersecurity frameworks now emphasize continuous adaptation—integrating real-time risk assessment, behavioral intelligence, and autonomous response capabilities. This shift reflects the fact that cyber risk is dynamic and cannot be mitigated solely by periodic assessments or manual controls.

Opinion

AI-centric cybersecurity frameworks represent a necessary evolution in defense strategy, not a temporary trend. The old model of perimeter defense and signature matching simply doesn’t scale in an era of massive data volumes, sophisticated AI-augmented threats, and 24/7 cloud operations. However, the promise of AI must be tempered with governance rigor. Organizations that treat AI as a magic bullet will face blind spots and risks—especially around privacy, explainability, and integration complexity.

Ultimately, the most effective AI cybersecurity frameworks will balance automated, real-time intelligence with human oversight and clear governance policies. This blend maximizes defensive value while mitigating potential misuse or operational failures.

AI Cybersecurity Framework — Summary

AI Cybersecurity framework provides a holistic approach to securing AI systems by integrating governance, risk management, and technical defense across the full AI lifecycle. It aligns with widely-accepted standards such as NIST RMF, ISO/IEC 42001, OWASP AI Security Top 10, and privacy regulations (e.g., GDPR, CCPA).

1️⃣ Govern

Set strategic direction and oversight for AI risk.

Goals: Define policies, accountability, and acceptable risk levels
Key Controls: AI governance board, ethical guidelines, compliance checks
Outcomes: Approved AI policies, clear governance structures, documented risk appetite

2️⃣ Identify

Understand what needs protection and the related risks.

Goals: Map AI assets, data flows, threat landscape
Key Controls: Asset inventory, access governance, threat modeling
Outcomes: Risk register, inventory map, AI threat profiles

3️⃣ Protect

Implement safeguards for AI data, models, and infrastructure.

Goals: Prevent unauthorized access and protect model integrity
Key Controls: Encryption, access control, secure development lifecycle
Outcomes: Hardened architecture, encrypted data, well-trained teams

4️⃣ Detect

Find signs of attack or malfunction in real time.

Goals: Monitor models, identify anomalies early
Key Controls: Logging, threat detection, model behavior monitoring
Outcomes: Alerts, anomaly reports, high-quality threat intelligence

5️⃣ Respond

Act quickly to contain and resolve security incidents.

Goals: Minimize damage and prevent escalation
Key Controls: Incident response plans, investigations, forensics
Outcomes: Detailed incident reports, corrective actions, improved readiness

6️⃣ Recover

Restore normal operations and reduce the chances of repeat incidents.

Goals: Service continuity and post-incident improvement
Key Controls: Backup and recovery, resilience testing
Outcomes: Restored systems and lessons learned that enhance resilience

Cross-Cutting Principles

These safeguards apply throughout all phases:

Ethics & Fairness: Reduce bias, ensure transparency
Explainability & Interpretability: Understand model decisions
Human-in-the-Loop: Oversight and accountability remain essential
Privacy & Security: Protect data by design

AI-Specific Threats Addressed

Adversarial attacks (poisoning, evasion)
Model theft and intellectual property loss
Data leakage and inference attacks
Bias manipulation and harmful outcomes

Overall Message

This framework ensures trustworthy, secure, and resilient AI operations by applying structured controls from design through incident recovery—combining cybersecurity rigor with ethical and responsible AI practices.

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps

Tags: AI-Driven Cybersecurity Frameworks

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Dec 25 2025

LLMs Are a Dead End: LeCun’s Break From Meta and the Future of AI

Category: AI,AI Governance,AI Guardrails — disc7 @ 3:24 pm

Yann LeCun — a pioneer of deep learning and Meta’s Chief AI Scientist — has left the company after shaping its AI strategy and influencing billions in investment. His departure is not a routine leadership change; it signals a deeper shift in how he believes AI must evolve.

LeCun is one of the founders of modern neural networks, a Turing Award recipient, and a core figure behind today’s deep learning breakthroughs. His work once appeared to be a dead end, yet it ultimately transformed the entire AI landscape.

Now, he is stepping away not to retire or join another corporate giant, but to create a startup focused on a direction Meta does not support. This choice underscores a bold statement: the current path of scaling Large Language Models (LLMs) may not lead to true artificial intelligence.

He argues that LLMs, despite their success, are fundamentally limited. They excel at predicting text but lack real understanding of the world. They cannot reason about physical reality, causality, or genuine intent behind events.

According to LeCun, today’s LLMs possess intelligence comparable to an animal — some say a cat — but even the cat has an advantage: it learns through real-world interaction rather than statistical guesswork.

His proposed alternative is what he calls World Models. These systems will learn like humans and animals do — by observing environments, experimenting, predicting outcomes, and refining internal representations of how the world works.

This approach challenges the current AI industry narrative that bigger models and more data alone will produce smarter, safer AI. Instead, LeCun suggests that a completely different foundation is required to achieve true machine intelligence.

Yet Meta continues investing enormous resources into scaling LLMs — the very AI paradigm he believes is nearing its limits. His departure raises an uncomfortable question about whether hype is leading strategic decisions more than science.

If he is correct, companies pushing ever-larger LLMs could face a major reckoning when progress plateaus and expectations fail to materialize.

My Opinion

LLMs are far from dead — they are already transforming industries and productivity. But LeCun highlights a real concern: scaling alone cannot produce human-level reasoning. The future likely requires a combination of both approaches — advanced language systems paired with world-aware learning. Instead of a dead end, this may be an inflection point where the AI field transitions toward deeper intelligence grounded in understanding, not just prediction.

Tags: LLM, Yann LeCun

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Dec 22 2025

Will AI Surpass Human Intelligence Soon? Examining the Race to the Singularity

Category: AI,AI Guardrails — disc7 @ 12:20 pm

Whether AI might surpass human intelligence in the next few years, based on recent trends and expert views — followed by my opinion:

Some recent analyses suggest that advances in AI capabilities may be moving fast enough that aspects of human‑level performance could be reached within a few years. One trend measurement — focusing on how quickly AI translation quality is improving compared to humans — shows steady progress and extrapolates that machine performance could equal human translators by the end of this decade if current trends continue. This has led to speculative headlines proposing that the technological singularity — the point where AI surpasses human intelligence in a broad sense — might occur within just a few years.

However, this type of projection is highly debated and depends heavily on how “intelligence” is defined and measured. Many experts emphasize that current AI systems, while powerful in narrow domains, are not yet near comprehensive general intelligence, and timelines vary widely. Surveys of AI researchers and more measured forecasts still often place true artificial general intelligence (AGI) — a prerequisite for singularity in many theories — much later, often around the 2030s or beyond.

There are also significant technical and conceptual challenges that make short‑term singularity predictions uncertain. Models today excel at specific tasks and show impressive abilities, but they lack the broad autonomy, self‑improvement capabilities, and general reasoning that many definitions of human‑level intelligence assume. Progress is real and rapid, yet experts differ sharply in timelines — some suggest near‑term breakthroughs, while others see more gradual advancement over decades.

My Opinion

I think it’s unlikely that AI will fully surpass human intelligence across all domains in the next few years. We are witnessing astonishing progress in certain areas — language, pattern recognition, generation, and task automation — but those achievements are still narrow compared to the full breadth of human cognition, creativity, and common‑sense reasoning. Broad, autonomous intelligence that consistently outperforms humans across contexts remains a formidable research challenge.

That said, AI will continue transforming industries and augmenting human capabilities, and we will likely see systems that feel very powerful in specialized tasks well before true singularity — perhaps by the late 2020s or early 2030s. The exact timeline will depend on breakthroughs we can’t yet predict, and it’s essential to prepare ethically and socially for the impacts even if singularity itself remains distant.

Tags: Singularity

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Dec 22 2025

Compliance Isn’t Security: Baseline Controls vs. Real-World Cyber Resilience

Category: Cyber resilience,Policies & Controls,Security Compliance,Security controls — disc7 @ 9:56 am

“Compliance isn’t security” debate

1. The core claim: Many cybersecurity professionals assert that compliance isn’t security — meaning simply meeting the letter of a standard (e.g., ISO 27001, ISO 42001, PCI, HIPAA, NIS, GDPR, DORA, Cyber Essentials) doesn’t by itself guarantee that an organization can withstand, detect, or recover from real-world attacks. Compliance frameworks typically define minimum baselines rather than prove operational resilience.

2. Why people feel this way: Critics argue that compliance programs often become checkbox exercises, focusing on documentation and audit artifacts rather than actual protective capability. Organizations can score well on audits and still suffer breaches because compliance doesn’t necessarily measure effectiveness of controls in practice.

3. Compliance vs security definitions: Compliance is essentially a benchmark against a standard — an organization either meets or fails certain requirements. Security, by contrast, is about managing risk dynamically and defending systems against evolving threats and adversaries. These two missions are related but fundamentally different in objectives and measurement.

4. The “baseline floor” perspective: Some practitioners push back on the notion that compliance has no value at all. They see compliance as providing a baseline floor of capabilities — a starting set of repeatable, measurable controls that help standardize expectations and reduce obvious, basic gaps that attackers exploit.

5. Compliance as structure: From this view, compliance frameworks give organizations a common language and structure to start measuring security efforts, track improvements over time, and communicate with boards, regulators, and insurers. Without structure, purely ad hoc security efforts can lack consistency and visibility.

6. The danger of complacency: The biggest practical risk isn’t compliance per se — it’s when organizations confuse passing an audit with being secure. Treating compliance as an end goal can create a false sense of safety, diverting resources from more effective defensive activities into chasing artifacts rather than outcomes.

7. Evolving threats vs static standards: Another common critique is that compliance frameworks often lag behind real-world threat evolution. Regulatory requirements typically update slowly, whereas attackers innovate constantly. As a result, meeting compliance may not sufficiently address emergent or advanced threats.

8. Complementary roles: Many experienced practitioners conclude that the healthiest view is neither compliance alone nor security alone. Compliance ensures visibility, documentation, and minimum control presence. Security builds on that baseline with active risk management, threat detection, and response mechanisms — which are necessary for meaningful protection.

9. Practical takeaway: In practice, compliance can serve as a foundation or enabler for security, but it should not be mistaken for security itself. Strong security programs often use compliance as a scaffolding — then extend beyond it with continuous improvement, automation, detection, response, and risk-based prioritization.

My Opinion

The statement “compliance isn’t security” is useful as a warning against complacency but overly simplistic if taken on its own. Compliance is not the security program; it’s often the starting point. Compliance frameworks help establish maturity, measure baseline controls, and satisfy regulatory or contractual requirements — all of which are valuable in risk management. However, true security requires active defense, continuous adaptation, and operational effectiveness that goes well beyond checkbox compliance. In short: compliance supports security, but it does not replace it — and treating it as an end goal can create blind spots that attackers will exploit.

Tags: Compliance ist't security

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Dec 22 2025

Securing Generative AI Usage in the Browser to Prevent Data Leakage

Category: AI,AI Governance,AI Governance Tools — disc7 @ 9:14 am

Here’s a rephrased and summarized version of the linked article organized into nine paragraphs, followed by my opinion at the end.

1️⃣ The Browser Has Become the Main AI Risk Vector
Modern workers increasingly use generative AI tools directly inside the browser, pasting emails, business files, and even source code into online AI assistants. Because traditional enterprise security tools weren’t built to monitor or understand this behavior, sensitive data often flows out of corporate control without detection.

2️⃣ Blocking AI Isn’t Realistic
Simply banning generative AI usage isn’t a workable solution. These tools offer productivity gains that employees and organizations find valuable. The article argues the real focus should be on securing how and where AI tools are used inside the browser session itself.

3️⃣ Understanding the Threat Model
The article outlines why browser-based AI interactions are uniquely risky: users routinely paste whole documents and proprietary data into prompt boxes, upload confidential files, and interact with AI extensions that have broad permission scopes. These behaviors create a threat surface that legacy defenses like firewalls and traditional DLP simply can’t see.

4️⃣ Policy Is the Foundation of Security
A strong security policy is described as the first step. Organizations should categorize which AI tools are sanctioned versus restricted and define what data types should never be entered into generative AI, such as financial records, regulated personal data, or source code. Enforcement matters: policies must be backed by browser-level controls, not just user guidance.

5️⃣ Isolation Reduces Risk Without Stopping Productivity
Instead of an all-or-nothing approach, teams can isolate risky workflows. For example, separate browser profiles or session controls can keep general AI usage away from sensitive internal applications. This lets employees use AI where appropriate while limiting accidental data exposure.

6️⃣ Data Controls at the Browser Edge
Technical data controls are critical to enforce policy. These include monitoring copy/paste actions, drag-and-drop events, and file uploads at the browser level before data ever reaches an external AI service. Tiered enforcement — from warnings to hard blocks — helps balance security with usability.

7️⃣ Managing AI Extensions Is Essential
Many AI-powered browser extensions require broad permissions — including read/modify page content — which can become covert data exfiltration channels if left unmanaged. The article emphasizes classifying and restricting such extensions based on risk.

8️⃣ Identity and Account Hygiene
Tying all sanctioned AI interactions back to corporate identities through single sign-on improves visibility and accountability. It also helps prevent situations where personal accounts or mixed browser contexts leak corporate data.

9️⃣ Visibility and Continuous Improvement
Lastly, strong telemetry — tracking what AI tools are accessed, what data is entered, and how often policy triggers occur — is essential to refine controls over time. Analytics can highlight risky patterns and help teams adjust policies and training for better outcomes.

My Opinion

This perspective is practical and forward-looking. Instead of knee-jerk bans on AI — which employees will circumvent — the article realistically treats the browser as the new security perimeter. That aligns with broader industry findings showing that browser-mediated AI usage is a major exfiltration channel and traditional security tools often miss it entirely.

However, implementing the recommended policies and controls isn’t trivial. It demands new tooling, tight integration with identity systems, and continuous monitoring, which many organizations struggle with today. But the payoff — enabling secure AI usage without crippling productivity — makes this a worthy direction to pursue. Secure AI adoption shouldn’t be about fear or bans, but about governance, visibility, and informed risk management.

Tags: AI Browser, Data leakage

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Dec 19 2025

ShareVault Achieves ISO 42001 Certification: Leading AI Governance in Virtual Data Rooms

Category: AI,AI Governance,ISO 42001 — disc7 @ 1:57 pm

ISO 42001 Certification by Leading AI Governance in Virtual Data Rooms

When your clients trust you with their most sensitive M&A documents, financial records, and confidential deal information, every security and compliance decision matters. ShareVault has taken a significant step beyond traditional data room security by achieving ISO 42001 certification—the international standard for AI management systems.

Why Financial Services and M&A Professionals Should Care

If you’re a deal advisor, investment banker, or private equity professional, you’re increasingly relying on AI-powered features in your virtual data room—intelligent document indexing, automated redaction suggestions, smart search capabilities, and analytics that surface insights from thousands of documents.

But how do you know these AI capabilities are managed responsibly? How can you be confident that:

AI systems won’t introduce bias into document classification or search results?
Algorithms processing sensitive financial data meet rigorous security standards?
Your confidential deal information isn’t being used to train AI models?
AI-driven recommendations are explainable and auditable for regulatory scrutiny?

ISO 42001 provides the answers. This comprehensive framework addresses AI-specific risks that traditional information security standards like ISO 27001 don’t fully cover.

ShareVault’s Commitment to AI Governance Excellence

ShareVault recognized early that as AI capabilities become more sophisticated in virtual data rooms, clients need assurance that goes beyond generic “we take security seriously” statements. The financial services and legal professionals who rely on ShareVault for billion-dollar transactions deserve verifiable proof of responsible AI management.

That commitment led ShareVault to pursue ISO 42001 certification—joining a select group of pioneers implementing the world’s first AI management system standard.

Building Trust Through Independent Verification

ShareVault engaged DISC InfoSec as an independent internal auditor specifically for ISO 42001 compliance. This wasn’t a rubber-stamp exercise. DISC InfoSec brought deep expertise in both AI governance frameworks and information security, conducting rigorous assessments of:

AI system lifecycle management – How ShareVault develops, deploys, monitors, and updates AI capabilities
Data governance for AI – Controls ensuring training data quality, protection, and appropriate use
Algorithmic transparency – Documentation and explainability of AI decision-making processes
Risk management – Identification and mitigation of AI-specific risks like bias, hallucinations, and unexpected outputs
Human oversight – Ensuring appropriate human involvement in AI-assisted processes

The internal audit process identified gaps, drove remediation efforts, and prepared ShareVault for external certification assessment—demonstrating a genuine commitment to AI governance rather than superficial compliance.

Certification Achieved: A Leadership Milestone

In 2025, ShareVault successfully completed both the Stage 1 and Stage 2 audits conducted by SenSiba, an accredited certification body. The Stage 1 audit validated ShareVault’s comprehensive documentation, policies, and procedures. The Stage 2 audit, completed in December 2025, examined actual implementation—verifying that controls operate effectively in practice, risks are actively managed, and continuous improvement processes function as designed.

ShareVault is now ISO 42001 certified—one of the first virtual data room providers to achieve this distinction. This certification reflects genuine leadership in responsible AI deployment, independently verified by external auditors with no stake in the outcome.

For financial services professionals, this means ShareVault’s AI governance approach has been rigorously assessed and certified against international standards, providing assurance that extends far beyond vendor claims.

What This Means for Your Deals

When you’re managing a $500 million acquisition or handling sensitive financial restructuring documents, you need more than promises about AI safety. ShareVault’s ISO 42001 certification provides tangible, verified assurance:

For M&A Advisors: Confidence that AI-powered document analytics won’t introduce errors or biases that could impact deal analysis or due diligence findings.

For Investment Bankers: Assurance that confidential client information processed by AI features remains protected and isn’t repurposed for model training or shared across clients.

For Legal Professionals: Auditability and explainability of AI-assisted document review and classification—critical when facing regulatory scrutiny or litigation.

For Private Equity Firms: Verification that AI capabilities in your deal rooms meet institutional-grade governance standards your LPs and regulators expect.

Why Industry Leadership Matters

The financial services industry faces increasing regulatory pressure regarding AI usage. The EU AI Act, SEC guidance on AI in financial services, and evolving state-level AI regulations all point toward a future where AI governance isn’t optional—it’s required.

ShareVault’s achievement of ISO 42001 certification demonstrates foresight that benefits clients in two critical ways:

Today: You gain immediate, certified assurance that AI capabilities in your data room meet rigorous governance standards, reducing your own AI-related risk exposure.

Tomorrow: As regulations tighten, you’re already working with a provider whose AI governance framework is certified against international standards, simplifying your own compliance efforts and protecting your competitive position.

The Bottom Line

For financial services and M&A professionals who demand the highest standards of security and compliance, ShareVault’s ISO 42001 certification represents more than a technical achievement—it’s independently verified proof of commitment to earning and maintaining your trust.

The rigorous process of implementation, independent internal auditing by DISC InfoSec, and successful completion of both Stage 1 and Stage 2 assessments by SenSiba demonstrates that ShareVault’s AI capabilities are deployed with certified safeguards, transparency, and accountability.

As deals become more complex and AI capabilities more sophisticated, partnering with a certified virtual data room provider that has proven its AI governance leadership isn’t just prudent—it’s essential to protecting your clients, your reputation, and your firm.

ShareVault’s investment in ISO 42001 certification means you can leverage powerful AI capabilities in your deal rooms with confidence that responsible management practices are independently certified and continuously maintained.

Ready to experience a virtual data room where AI innovation meets certified governance? Contact ShareVault to learn how ISO 42001-certified AI management protects your most sensitive transactions.

Tags: ISO 42001 certificate, Sharevault

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Dec 16 2025

A Simple 4-Step Path to ISO 42001 for SMBs

Category: AI,AI Governance,ISO 42001 — disc7 @ 9:49 am

A Simple 4-Step Path to ISO 42001 for SMBs

Practical AI Governance for Compliance, Risk, and Security Leaders

Artificial Intelligence is moving fast—but regulations, customer expectations, and board-level scrutiny are moving even faster. ISO/IEC 42001 gives organizations a structured way to govern AI responsibly, securely, and in alignment with laws like the EU AI Act.

For SMBs, the good news is this: ISO 42001 does not require massive AI programs or complex engineering changes. At its core, it follows a clear four-step process that compliance, risk, and security teams already understand.

Step 1: Define AI Scope and Governance Context

The first step is understanding where and how AI is used in your business. This includes internally developed models, third-party AI tools, SaaS platforms with embedded AI, and even automation driven by machine learning.

For SMBs, this step is about clarity—not perfection. You define:

What AI systems are in scope
Business objectives and constraints
Regulatory, contractual, and ethical expectations
Roles and accountability for AI decisions

This mirrors how ISO 27001 defines ISMS scope, making it familiar for security and compliance teams.

Step 2: Identify and Assess AI Risks

Once AI usage is defined, the focus shifts to risk identification and impact assessment. Unlike traditional cyber risk, AI introduces new concerns such as bias, model drift, lack of explainability, data misuse, and unintended outcomes.

In this step, organizations:

Identify AI-specific risks across the lifecycle
Evaluate business, legal, and security impact
Consider affected stakeholders (customers, employees, regulators)
Prioritize risks based on likelihood and severity

This step aligns closely with enterprise risk management and can be integrated into existing risk registers.

Step 3: Implement AI Controls and Lifecycle Management

With risks prioritized, the organization selects practical governance and security controls. ISO 42001 does not prescribe one-size-fits-all solutions—it focuses on proportional controls based on risk.

Typical activities include:

AI policies and acceptable use guidelines
Human oversight and approval checkpoints
Data governance and model documentation
Secure development and vendor due diligence
Change management for AI updates

For SMBs, this is about leveraging existing ISO 27001, SOC 2, or NIST-aligned controls and extending them to cover AI.

Step 4: Monitor, Audit, and Improve

AI governance is not a one-time exercise. The final step ensures continuous monitoring, review, and improvement as AI systems evolve.

This includes:

Ongoing performance and risk monitoring
Internal audits and management reviews
Incident handling and corrective actions
Readiness for certification or regulatory review

This step closes the loop and ensures AI governance stays aligned with business growth and regulatory change.

Why This Matters for SMBs

Regulators and customers are no longer asking if you use AI—they’re asking how you govern it. ISO 42001 provides a defensible, auditable framework that shows due diligence without slowing innovation.

How DISC InfoSec Can Help

DISC InfoSec helps SMBs implement ISO 42001 quickly, pragmatically, and cost-effectively—especially if you’re already aligned with ISO 27001, SOC 2, or NIST. We translate AI risk into business language, reuse what you already have, and guide you from scoping to certification readiness.

👉 Talk to DISC InfoSec to build AI governance that satisfies regulators, reassures customers, and supports safe AI adoption—without unnecessary complexity.

Tufte_iso42001_pdf

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Tags: 4-Step Path to ISO 42001

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Opinion

Self-assessment tools

Types of Risk & Risk Assessment

Opinion: Why Knowing Risk Types Helps Businesses

EU AI Act: Why Every Organization Using AI Must Pay Attention

How DISC InfoSec Helps You Stay Ahead

Earlier posts covering the EU AI Act

12 Pillars of Cybersecurity — Simplified Overview — Start by getting the basics right — it’s the foundation of every effective security program.

⭐ Expert Opinion

Opinion

AI Cybersecurity Framework — Summary

1️⃣ Govern

2️⃣ Identify

3️⃣ Protect

4️⃣ Detect

5️⃣ Respond

6️⃣ Recover

Cross-Cutting Principles

AI-Specific Threats Addressed

Overall Message

My Opinion

My Opinion

My Opinion

ISO 42001 Certification by Leading AI Governance in Virtual Data Rooms

Why Financial Services and M&A Professionals Should Care

ShareVault’s Commitment to AI Governance Excellence

Building Trust Through Independent Verification

Certification Achieved: A Leadership Milestone

What This Means for Your Deals

Why Industry Leadership Matters

The Bottom Line

A Simple 4-Step Path to ISO 42001 for SMBs

Step 1: Define AI Scope and Governance Context

Step 2: Identify and Assess AI Risks

Step 3: Implement AI Controls and Lifecycle Management

Step 4: Monitor, Audit, and Improve

Why This Matters for SMBs

How DISC InfoSec Can Help

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