DISC InfoSec blogAI Archives | Page 8 of 10

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

Comments (3)

Feb 26 2025

Why data provenance is important for AI system

Category: AI — disc7 @ 10:50 am

Data annotation, in which the significant elements of the data are added as metadata (e.g. information
about data provenance or labels to aid with training a model)

Data provenance is crucial for AI systems because it ensures trust, accountability, and reliability in the data used for training and decision-making. Here’s why it matters:

Data Quality & Integrity – Knowing the source of data helps verify its accuracy and reliability, reducing biases and errors in AI models.
Regulatory Compliance – Many laws (e.g., GDPR, HIPAA) require organizations to track data origins and transformations to ensure compliance.
Bias Detection & Mitigation – Understanding data lineage helps identify and correct biases that could lead to unfair AI outcomes.
Reproducibility – AI models should produce consistent results under similar conditions; data provenance enables reproducibility by tracking inputs and transformations.
Security & Risk Management – Provenance helps detect unauthorized modifications, ensuring data integrity and reducing risks of poisoning attacks.
Ethical AI & Transparency – Clear documentation of data sources fosters trust in AI decisions, making them more explainable and accountable.

In short, data provenance is a foundational pillar for trustworthy, compliant, and ethical AI systems.

Checkout DISC InfoSec previous posts on AI topic

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.

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

Tags: data provenance

Comments (1)

Feb 23 2025

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

Category: AI,Information Security — disc7 @ 10:50 pm

AI is reshaping industries by automating routine tasks, processing and analyzing vast amounts of data, and enhancing decision-making capabilities. Its ability to identify patterns, generate insights, and optimize processes enables businesses to operate more efficiently and strategically. However, along with its numerous advantages, AI also presents challenges such as ethical concerns, bias in algorithms, data privacy risks, and potential job displacement. By gaining a comprehensive understanding of AI’s fundamentals, as well as its risks and benefits, we can leverage its potential responsibly to foster innovation, drive sustainable growth, and create positive societal impact.

This serves as a template for evaluating internal and external business objectives (market needs) within the given context, ultimately aiding in defining the right scope for the organization.

Why Clause 4 in ISO 42001 is Critical for Success

Clause 4 (Context of the Organization) in ISO/IEC 42001 is fundamental because it sets the foundation for an effective AI Management System (AIMS). If this clause is not properly implemented, the entire AI governance framework could be misaligned with business objectives, regulatory requirements, and stakeholder expectations.

1. It Defines the Scope and Direction of AI Governance

Clause 4.1 – Understanding the Organization and Its Context ensures that AI governance is tailored to the organization’s specific risks, objectives, and industry landscape.

Without it: The AI strategy might be disconnected from business priorities.
With it: AI implementation is aligned with organizational goals, compliance, and risk management.

Clause 4 of ISO/IEC 42001:2023 (AI Management System Standard) focuses on the context of the organization. This clause requires organizations to define internal and external factors that influence their AI management system (AIMS). Here’s a breakdown of its key components:

1. Understanding the Organization and Its Context (4.1)

Identify external and internal issues that affect the AI Management System.
External factors may include regulatory landscape, industry trends, societal expectations, and technological advancements.
Internal factors can involve corporate policies, organizational structure, resources, and AI capabilities.

2. Understanding the Needs and Expectations of Stakeholders (4.2)

Identify stakeholders (customers, regulators, employees, suppliers, etc.).
Determine their needs, expectations, and concerns related to AI use.
Consider legal, regulatory, and contractual requirements.

3. Determining the Scope of the AI Management System (4.3)

Define the boundaries and applicability of AIMS based on identified factors.
Consider organizational units, functions, and jurisdictions in scope.
Ensure alignment with business objectives and compliance obligations.

4. AI Management System (AIMS) and Its Implementation (4.4)

Establish, implement, maintain, and continuously improve the AIMS.
Ensure it aligns with organizational goals and risk management practices.
Integrate AI governance, ethics, risk, and compliance into business operations.

Why This Matters

Clause 4 ensures that organizations build their AI governance framework with a strong foundation, considering all relevant factors before implementing AI-related controls. It aligns AI initiatives with business strategy, regulatory compliance, and stakeholder expectations.

Here are the options:

4.1 – Understanding the Organization and Its Context
4.2 – Understanding the Needs and Expectations of Stakeholders
4.3 – Determining the Scope of the AI Management System (AIMS)
4.4 – AI Management System (AIMS) and Its Implementation

Breakdown of “Understanding the Organization and its context”

Detailed Breakdown of Clause 4.1 – Understanding the Organization and Its Context (ISO 42001)

Clause 4.1 of ISO/IEC 42001:2023 requires an organization to determine internal and external factors that can affect its AI Management System (AIMS). This understanding helps in designing an effective AI governance framework.

1. Purpose of Clause 4.1

The main goal is to ensure that AI-related risks, opportunities, and strategic objectives align with the organization’s broader business environment. Organizations need to consider:

How AI impacts their operations.
What external and internal factors influence AI adoption, governance, and compliance.
How these factors shape the effectiveness of AIMS.

2. Key Requirements

Organizations must:

Identify External Issues:
These are factors outside the organization that can impact AI governance, including:
- Regulatory & Legal Landscape – AI laws, data protection (e.g., GDPR, AI Act), industry standards.
- Technological Trends – Advancements in AI, ML frameworks, cloud computing, cybersecurity.
- Market & Competitive Landscape – Competitor AI adoption, emerging business models.
- Social & Ethical Concerns – Public perception, ethical AI principles (bias, fairness, transparency).
Identify Internal Issues:
These factors exist within the organization and influence AIMS, such as:
- AI Strategy & Objectives – Business goals for AI implementation.
- Organizational Structure – AI governance roles, responsibilities, leadership commitment.
- Capabilities & Resources – AI expertise, financial resources, infrastructure.
- Existing Policies & Processes – AI ethics policies, risk management frameworks.
- Data Governance & Security – Data availability, quality, security, and compliance.
Monitor & Review These Issues:
- These factors are dynamic and should be reviewed regularly.
- Organizations should track changes in external regulations, AI advancements, and internal policies.

3. Practical Implementation Steps

Conduct a PESTLE Analysis (Political, Economic, Social, Technological, Legal, Environmental) to map external factors.
Perform an Internal SWOT Analysis (Strengths, Weaknesses, Opportunities, Threats) for AI capabilities.
Engage Stakeholders (leadership, compliance, IT, data science teams) in discussions about AI risks and objectives.
Document Findings in an AI context assessment report to support AIMS planning.

4. Why It Matters

Clause 4.1 ensures that AI governance is not isolated but integrated into the organization’s strategic, operational, and compliance frameworks. A strong understanding of context helps in:
✅ Reducing AI-related risks (bias, security, regulatory non-compliance).
✅ Aligning AI adoption with business goals and ethical considerations.
✅ Preparing for evolving AI regulations and market demands.

Implementation Examples & Templates for Clause 4.1 (Understanding the Organization and Its Context) in ISO 42001

Here are practical examples and a template to help document and implement Clause 4.1 effectively.

1. Example: AI Governance in a Financial Institution

Scenario:

A bank is implementing an AI-based fraud detection system and needs to assess its internal and external context.

Step 1: Identify External Issues

Category	Identified Issues
Regulatory & Legal	GDPR, AI Act (EU), banking compliance rules.
Technological Trends	ML advancements in fraud detection, cloud AI.
Market Competition	Competitors adopting AI-driven risk assessment.
Social & Ethical	AI bias concerns in fraud detection models.

Step 2: Identify Internal Issues

Category	Identified Issues
AI Strategy	Improve fraud detection efficiency by 30%.
Organizational Structure	AI governance committee oversees compliance.
Resources	AI team with data scientists and compliance experts.
Policies & Processes	Data retention policy, ethical AI guidelines.

Step 3: Continuous Monitoring & Review

Quarterly regulatory updates for AI laws.
Ongoing performance evaluation of AI fraud detection models.
Stakeholder feedback sessions on AI transparency and fairness.

2. Template: AI Context Assessment Document

Use this template to document the context of your organization.

AI Context Assessment Report

📌 Organization Name: [Your Organization]
📌 Date: [MM/DD/YYYY]
📌 Prepared By: [Responsible Person/Team]

1. External Factors Affecting AI Management System

Factor Type	Description
Regulatory & Legal	[List relevant laws & regulations]
Technological Trends	[List emerging AI technologies]
Market Competition	[Describe AI adoption by competitors]
Social & Ethical Concerns	[Mention AI ethics, bias, transparency challenges]

2. Internal Factors Affecting AI Management System

Factor Type	Description
AI Strategy & Objectives	[Define AI goals & business alignment]
Organizational Structure	[List AI governance roles]
Resources & Expertise	[Describe team skills, tools, and funding]
Data Governance	[Outline data security, privacy, and compliance]

3. Monitoring & Review Process

Frequency of Review: [Monthly/Quarterly/Annually]
Responsible Team: [AI Governance Team / Compliance]
Methods: [Stakeholder meetings, compliance audits, AI performance reviews]

Next Steps

✅ Integrate this assessment into your AI Management System (AIMS).
✅ Update it regularly based on changing laws, risks, and market trends.
✅ Ensure alignment with ISO 42001 compliance and business goals.

Keep in mind that you can refine your context and expand your scope during your next internal/surveillance audit.

Managing Artificial Intelligence Threats with ISO 27001

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

Some AI frameworks have remote code execution as a feature – explore common attack vectors and mitigation strategies

Basic Principle to Enterprise AI Security

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

New regulations and AI hacks drive cyber security changes in 2025

Threat modeling your generative AI workload to evaluate security risk

How CISOs Can Drive the Adoption of Responsible AI Practices

Hackers will use machine learning to launch attacks

To fight AI-generated malware, focus on cybersecurity fundamentals

4 ways AI is transforming audit, risk and compliance

ISMS and ISO 27k training

ISO certification training courses.

🚀 Unlock Your AI Governance Expertise with ISO 42001! 🎯

✅ 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.

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

Tags: ISO 42001, ISO 42001 Clause 4, ISO 42001 Foundation, ISo 42001 Lead Auditor, ISO 42001 lead Implementer

Comments (2)

Feb 13 2025

Managing Artificial Intelligence Threats with ISO 27001

Category: AI,ISO 27k — disc7 @ 9:43 am

Artificial intelligence (AI) and machine learning (ML) systems are increasingly integral to business operations, but they also introduce significant security risks. Threats such as malware attacks or the deliberate insertion of misleading data into inadequately designed AI/ML systems can compromise data integrity and lead to the spread of false information. These incidents may result in severe consequences, including legal actions, financial losses, increased operational and insurance costs, diminished competitiveness, and reputational damage.

To mitigate AI-related security threats, organizations can implement specific controls outlined in ISO 27001. Key controls include:

A.5.9 Inventory of information and other associated assets: Maintaining a comprehensive inventory of information assets ensures that all AI/ML components are identified and managed appropriately.
A.5.12 Information classification: Classifying information processed by AI systems helps in applying suitable protection measures based on sensitivity and criticality.
A.5.14 Information transfer: Securing the transfer of data to and from AI systems prevents unauthorized access and data breaches.
A.5.15 Access control: Implementing strict access controls ensures that only authorized personnel can interact with AI systems and the data they process.
A.5.19 Information security in supplier relationships: Managing security within supplier relationships ensures that third-party providers handling AI components adhere to the organization’s security requirements.
A.5.31 Legal, statutory, regulatory, and contractual requirements: Complying with all relevant legal and regulatory obligations related to AI systems prevents legal complications.
A.8.25 Secure development life cycle: Integrating security practices throughout the AI system development life cycle ensures that security is considered at every stage, from design to deployment.

By implementing these controls, organizations can effectively manage the confidentiality, integrity, and availability of information processed by AI systems. This proactive approach not only safeguards against potential threats but also enhances overall information security posture.

In addition to these controls, organizations should conduct regular risk assessments to identify and address emerging AI-related threats. Continuous monitoring and updating of security measures are essential to adapt to the evolving landscape of AI technologies and associated risks.

Furthermore, fostering a culture of security awareness among employees, including training on AI-specific threats and best practices, can significantly reduce the likelihood of security incidents. Engaging with industry standards and staying informed about regulatory developments related to AI will also help organizations maintain compliance and strengthen their security frameworks.

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

Some AI frameworks have remote code execution as a feature – explore common attack vectors and mitigation strategies

Basic Principle to Enterprise AI Security

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

New regulations and AI hacks drive cyber security changes in 2025

Threat modeling your generative AI workload to evaluate security risk

How CISOs Can Drive the Adoption of Responsible AI Practices

Hackers will use machine learning to launch attacks

To fight AI-generated malware, focus on cybersecurity fundamentals

4 ways AI is transforming audit, risk and compliance

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

Tags: Artificial Intelligence Threats

Comments (11)

Feb 12 2025

Some AI frameworks have remote code execution as a feature – explore common attack vectors and mitigation strategies

Category: AI,Remote code — disc7 @ 7:45 am

Some AI frameworks and platforms support remote code execution (RCE) as a feature, often for legitimate use cases like distributed computing, model training, and inference. However, this can also pose security risks if not properly secured. Here are some notable examples:

1. AI Frameworks with Remote Execution Features

A. Jupyter Notebooks

Jupyter supports remote kernel execution, allowing users to run code on a remote server while interacting via a local browser.
If improperly configured (e.g., running on an open network without authentication), it can expose an unauthorized RCE risk.

B. Ray (for Distributed AI Computing)

Ray allows distributed execution of Python tasks across multiple nodes.
It enables remote function execution (@ray.remote) for parallel processing in machine learning workloads.
Misconfigured Ray clusters can be exploited for unauthorized code execution.

C. TensorFlow Serving & TorchServe

These frameworks execute model inference remotely, often exposing APIs for inference requests.
If the API allows arbitrary input (e.g., executing scripts inside the model environment), it can lead to RCE vulnerabilities.

D. Kubernetes & AI Workloads

AI workloads are often deployed in Kubernetes clusters, which allow remote execution via kubectl exec.
If Kubernetes RBAC is misconfigured, attackers could execute arbitrary code on AI nodes.

2. Platforms Offering Remote Code Execution

A. Google Colab

Allows users to execute Python code on remote GPUs/TPUs.
Though secure, running untrusted notebooks could execute malicious code remotely.

B. OpenAI API, Hugging Face Inference API

These platforms run AI models remotely and expose APIs for users.
They don’t expose direct RCE, but poorly designed API endpoints could introduce security risks.

3. Security Risks & Mitigations

Risk	Mitigation
Unauthenticated remote access (e.g., Jupyter, Ray)	Enable authentication & restrict network access
Arbitrary code execution via AI APIs	Implement input validation & sandboxing
Misconfigured Kubernetes clusters	Enforce RBAC & limit exec privileges
Untrusted model execution (e.g., Colab, TorchServe)	Run models in isolated environments

Securing AI Workloads Against Remote Code Execution (RCE) Risks

AI workloads often involve remote execution of code, whether for model training, inference, or distributed computing. If not properly secured, these environments can be exploited for unauthorized code execution, leading to data breaches, malware injection, or full system compromise.

1. Common AI RCE Attack Vectors & Mitigation Strategies

Attack Vector	Risk	Mitigation
Jupyter Notebook Exposed Over the Internet	Unauthorized access to the environment, remote code execution	✅ Use strong authentication (token-based or OAuth) ✅ Restrict access to trusted IPs ✅ Disable root execution
Ray or Dask Cluster Misconfiguration	Attackers can execute arbitrary functions across nodes	✅ Use firewall rules to limit access ✅ Enforce TLS encryption between nodes ✅ Require authentication for remote task execution
Compromised Model File (ML Supply Chain Attack)	Malicious models can execute arbitrary code on inference	✅ Scan models for embedded scripts ✅ Run inference in an isolated environment (Docker/sandbox)
Unsecured AI APIs (TensorFlow Serving, TorchServe)	API could allow command injection through crafted inputs	✅ Implement strict input validation ✅ Run API endpoints with least privilege
Kubernetes Cluster with Weak RBAC	Attackers gain access to AI pods and execute commands	✅ Restrict `kubectl exec` privileges ✅ Use Kubernetes Network Policies to limit communication ✅ Rotate service account credentials
Serverless AI Functions (AWS Lambda, GCP Cloud Functions)	Code execution environment can be exploited via unvalidated input	✅ Use IAM policies to restrict execution rights ✅ Validate API payloads before execution

2. Best Practices for Securing AI Workloads

A. Secure Remote Execution in Jupyter Notebooks

Jupyter Notebooks are often used for AI development and testing but can be exploited if left exposed.

🔹 Recommended Configurations:
✅ Enable password authentication:

bashCopyEditjupyter notebook --generate-config

Edit jupyter_notebook_config.py:

pythonCopyEditc.NotebookApp.password = 'hashed_password'

✅ Restrict access to localhost (--ip=127.0.0.1)
✅ Run Jupyter inside a container (Docker, Kubernetes)
✅ Use VPN or SSH tunneling instead of exposing ports

B. Lock Down Kubernetes & AI Workloads

Many AI frameworks (TensorFlow, PyTorch, Ray) run in Kubernetes, where misconfigurations can lead to container escapes and lateral movement.

🔹 Key Security Measures:
✅ Restrict kubectl exec privileges to prevent unauthorized command execution:

yamlCopyEditapiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: default
  name: restrict-exec
rules:
- apiGroups: [""]
  resources: ["pods/exec"]
  verbs: ["get"]

✅ Enforce Pod Security Policies (disable privileged containers, enforce seccomp profiles)
✅ Limit AI workloads to isolated namespaces

Tags: Adversarial AI Attacks, AI framwork, Remote Code Execution

Comments (3)

Feb 07 2025

GhostGPT Released – AI Tool Enables Malicious Code Generation

Category: AI — disc7 @ 9:07 am

GhostGPT is a new artificial intelligence (AI) tool that cybercriminals are exploiting to develop malicious software, breach systems, and craft convincing phishing emails. According to security researchers from Abnormal Security, GhostGPT is being sold on the messaging platform Telegram, with prices starting at $50 per week. Its appeal lies in its speed, user-friendliness, and the fact that it doesn’t store user conversations, making it challenging for authorities to trace activities back to individuals.

This trend isn’t isolated to GhostGPT; other AI tools like WormGPT are also being utilized for illicit purposes. These unethical AI models enable criminals to circumvent the security measures present in legitimate AI systems such as ChatGPT, Google Gemini, Claude, and Microsoft Copilot. The emergence of cracked AI models—modified versions of authentic AI tools—has further facilitated hackers’ access to powerful AI capabilities without restrictions. Security experts have observed a rise in the use of these tools for cybercrime since late 2024, posing significant concerns for the tech industry and security professionals. The misuse of AI in this manner threatens both businesses and individuals, as AI was intended to assist rather than harm.

For further details, access the article here

Basic Principle to Enterprise AI Security

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

New regulations and AI hacks drive cyber security changes in 2025

Threat modeling your generative AI workload to evaluate security risk

How CISOs Can Drive the Adoption of Responsible AI Practices

Hackers will use machine learning to launch attacks

To fight AI-generated malware, focus on cybersecurity fundamentals

4 ways AI is transforming audit, risk and compliance

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

Tags: GhostGPT, Malicious code

Comments (0)

Jan 29 2025

Basic Principle to Enterprise AI Security

Category: AI — disc7 @ 12:24 pm

Securing AI in the Enterprise: A Step-by-Step Guide

Establish AI Security Ownership
Organizations must define clear ownership and accountability for AI security. Leadership should decide whether AI governance falls under a cross-functional committee, IT/security teams, or individual business units. Establishing policies, defining decision-making authority, and ensuring alignment across departments are key steps in successfully managing AI security from the start.
Identify and Mitigate AI Risks
AI introduces unique risks, including regulatory compliance challenges, data privacy vulnerabilities, and algorithmic biases. Organizations must evaluate legal obligations (such as GDPR, HIPAA, and the EU AI Act), implement strong data protection measures, and address AI transparency concerns. Risk mitigation strategies should include continuous monitoring, security testing, clear governance policies, and incident response plans.
Adopt AI Security Best Practices
Businesses should follow security best practices, such as starting with small AI implementations, maintaining human oversight, establishing technical guardrails, and deploying continuous monitoring. Strong cybersecurity measures—such as encryption, access controls, and regular security audits—are essential. Additionally, comprehensive employee training programs help ensure responsible AI usage.
Assess AI Needs and Set Measurable Goals
AI implementation should align with business objectives, with clear milestones set for six months, one year, and beyond. Organizations should define success using key performance indicators (KPIs) such as revenue impact, efficiency improvements, and compliance adherence. Both quantitative and qualitative metrics should guide AI investments and decision-making.
Evaluate AI Tools and Security Measures
When selecting AI tools, organizations must assess security, accuracy, scalability, usability, and compliance. AI solutions should have strong data protection mechanisms, clear ROI, and effective customization options. Evaluating AI tools using a structured approach ensures they meet security and business requirements.
Purchase and Implement AI Securely
Before deploying AI solutions, businesses must ask key questions about effectiveness, performance, security, scalability, and compliance. Reviewing trial options, pricing models, and regulatory alignment (such as GDPR or CCPA compliance) is critical to selecting the right AI tool. AI security policies should be integrated into the organization’s broader cybersecurity framework.
Launch an AI Pilot Program with Security in Mind
Organizations should begin with a controlled AI pilot to assess risks, validate performance, and ensure compliance before full deployment. This includes securing high-quality training data, implementing robust authentication controls, continuously monitoring performance, and gathering user feedback. Clear documentation and risk management strategies will help refine AI adoption in a secure and scalable manner.

By following these steps, enterprises can securely integrate AI, protect sensitive data, and ensure regulatory compliance while maximizing AI’s potential.

New regulations and AI hacks drive cyber security changes in 2025

Threat modeling your generative AI workload to evaluate security risk

How CISOs Can Drive the Adoption of Responsible AI Practices

Hackers will use machine learning to launch attacks

To fight AI-generated malware, focus on cybersecurity fundamentals

4 ways AI is transforming audit, risk and compliance

Tags: AI Governance, AI privacy, AI Risk Management, AI security

Threat modeling your generative AI workload to evaluate security risk

Jan 22 2025

New regulations and AI hacks drive cyber security changes in 2025

Category: AI,Cyber Strategy,Hacking — disc7 @ 10:57 am

The article discusses how evolving regulations and AI-driven cyberattacks are reshaping the cybersecurity landscape. Key points include:

New Regulations: Governments are introducing stricter cybersecurity regulations, pushing organizations to enhance their compliance and risk management strategies.
AI-Powered Cyberattacks: The rise of AI is enabling more sophisticated attacks, such as automated phishing and advanced malware, forcing companies to adopt proactive defense measures.
Evolving Cybersecurity Strategies: Businesses are prioritizing the integration of AI-driven tools to bolster their security posture, focusing on threat detection, mitigation, and overall resilience.

Organizations must adapt quickly to address these challenges, balancing regulatory compliance with advanced technological solutions to stay secure.

For further details, access the article here

How CISOs Can Drive the Adoption of Responsible AI Practices

Hackers will use machine learning to launch attacks

To fight AI-generated malware, focus on cybersecurity fundamentals

4 ways AI is transforming audit, risk and compliance

AI security bubble already springing leaks

The Rise of AI Bots: Understanding Their Impact on Internet Security

How to Address AI Security Risks With ISO 27001

AI cybersecurity needs to be as multi-layered as the system it’s protecting

How cyber criminals are compromising AI software supply chains

AI Risk Management

Tags: AI hacks, Cyber Strategy

Comments (8)

Nov 19 2024

Threat modeling your generative AI workload to evaluate security risk

Category: AI,Risk Assessment — disc7 @ 8:40 am

AWS emphasizes the importance of threat modeling for securing generative AI workloads, focusing on balancing risk management and business outcomes. A robust threat model is essential across the AI lifecycle stages, including design, deployment, and operations. Risks specific to generative AI, such as model poisoning and data leakage, need proactive mitigation, with organizations tailoring risk tolerance to business needs. Regular testing for vulnerabilities, like malicious prompts, ensures resilience against evolving threats.

Generative AI applications follow a structured lifecycle, from identifying business objectives to monitoring deployed models. Security considerations should be integral from the start, with measures like synthetic threat simulations during testing. For applications on AWS, leveraging its security tools, such as Amazon Bedrock and OpenSearch, helps enforce role-based access controls and prevent unauthorized data exposure.

AWS promotes building secure AI solutions on its cloud, which offers over 300 security services. Customers can utilize AWS infrastructure’s compliance and privacy frameworks while tailoring controls to organizational needs. For instance, techniques like Retrieval-Augmented Generation ensure sensitive data is redacted before interaction with foundational models, minimizing risks.

Threat modeling is described as a collaborative process involving diverse roles—business stakeholders, developers, security experts, and adversarial thinkers. Consistency in approach and alignment with development workflows (e.g., Agile) ensures scalability and integration. Using existing tools for collaboration and issue tracking reduces friction, making threat modeling a standard step akin to unit testing.

Organizations are urged to align security practices with business priorities while maintaining flexibility. Regular audits and updates to models and controls help adapt to the dynamic AI threat landscape. AWS provides reference architectures and security matrices to guide organizations in implementing these best practices efficiently.

*Threat composer threat statement builder*

You can write and document these possible threats to your application in the form of threat statements. Threat statements are a way to maintain consistency and conciseness when you document your threat. At AWS, we adhere to a threat grammar which follows the syntax:

A [threat source] with [prerequisites] can [threat action] which leads to [threat impact], negatively impacting [impacted assets].

This threat grammar structure helps you to maintain consistency and allows you to iteratively write useful threat statements. As shown in Figure 2, Threat Composer provides you with this structure for new threat statements and includes examples to assist you.

You can read the full article here

Proactive governance is a continuous process of risk and threat identification, analysis and remediation. In addition, it also includes proactively updating policies, standards and procedures in response to emerging threats or regulatory changes.

OWASP updated 2025 Top 10 Risks for Large Language Models (LLMs), a crucial resource for developers, security teams, and organizations working with AI.

How CISOs Can Drive the Adoption of Responsible AI Practices

The CISO’s Guide to Securing Artificial Intelligence

AI in Cyber Insurance: Risk Assessments and Coverage Decisions

Hackers will use machine learning to launch attacks

To fight AI-generated malware, focus on cybersecurity fundamentals

4 ways AI is transforming audit, risk and compliance

AI security bubble already springing leaks

The Rise of AI Bots: Understanding Their Impact on Internet Security

How to Address AI Security Risks With ISO 27001

Comprehensive vCISO Services

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: LLM, OWASP, Threat modeling

The CISO’s Guide to Securing Artificial Intelligence

Nov 13 2024

How CISOs Can Drive the Adoption of Responsible AI Practices

Category: AI,Information Security — disc7 @ 11:47 am

Amid the rush to adopt AI, leaders face significant risks if they lack an understanding of the technology’s potential cyber threats. A PwC survey revealed that 40% of global leaders are unaware of generative AI’s risks, posing potential vulnerabilities. CISOs should take a leading role in assessing, implementing, and overseeing AI, as their expertise in risk management can ensure safer integration and focus on AI’s benefits. While some advocate for a chief AI officer, security remains integral, emphasizing the CISO’s/ vCISO’S strategic role in guiding responsible AI adoption.

CISOs are crucial in managing the security and compliance of AI adoption within organizations, especially with evolving regulations. Their role involves implementing a security-first approach and risk management strategies, which includes aligning AI goals through an AI consortium, collaborating with cybersecurity teams, and creating protective guardrails.

They guide acceptable risk tolerance, manage governance, and set controls for AI use. Whether securing AI consumption or developing solutions, CISOs must stay updated on AI risks and deploy relevant resources.

A strong security foundation is essential, involving comprehensive encryption, data protection, and adherence to regulations like the EU AI Act. CISOs enable informed cross-functional collaboration, ensuring robust monitoring and swift responses to potential threats.

As AI becomes mainstream, organizations must integrate security throughout the AI lifecycle to guard against GenAI-driven cyber threats, such as social engineering and exploitation of vulnerabilities. This requires proactive measures and ongoing workforce awareness to counter these challenges effectively.

“AI will touch every business function, even in ways that have yet to be predicted. As the bridge between security efforts and business goals, CISOs serve as gatekeepers for quality control and responsible AI use across the business. They can articulate the necessary ground for security integrations that avoid missteps in AI adoption and enable businesses to unlock AI’s full potential to drive better, more informed business outcomes. “

You can read the full article here

CISOs play a pivotal role in guiding responsible AI adoption to balance innovation with security and compliance. They need to implement security-first strategies and align AI goals with organizational risk tolerance through stakeholder collaboration and robust risk management frameworks. By integrating security throughout the AI lifecycle, CISOs/vCISOs help protect critical assets, adhere to regulations, and mitigate threats posed by GenAI. Vigilance against AI-driven attacks and fostering cross-functional cooperation ensures that organizations are prepared to address emerging risks and foster safe, strategic AI use.

Need expert guidance? Book a free 30-minute consultation with a vCISO.

Comprehensive vCISO Services

Hackers will use machine learning to launch attacks

To fight AI-generated malware, focus on cybersecurity fundamentals

4 ways AI is transforming audit, risk and compliance

AI security bubble already springing leaks

The Rise of AI Bots: Understanding Their Impact on Internet Security

How to Address AI Security Risks With ISO 27001

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: AI privacy, AI security impact, AI threats, CISO, vCISO

Machine Learning: 3 books in 1: – Hacking Tools for Computer + Hacking With Kali Linux + Python Programming- The ultimate beginners guide to improve your knowledge of programming and data science

Nov 06 2024

Hackers will use machine learning to launch attacks

Category: AI,Hacking — disc7 @ 1:37 pm

The article on CSO Online covers how hackers may leverage machine learning for cyber attacks, including methods like automating social engineering, enhancing malware evasion, launching advanced spear-phishing, and creating adaptable attack strategies that evolve with new data. Machine learning could also help attackers mimic human behavior to bypass security protocols and tailor attacks based on behavioral analysis. This evolving threat landscape underscores the importance of proactive, ML-driven security defenses.

The article covers key ways hackers could leverage machine learning to enhance their cyberattacks:

Sophisticated Phishing: Machine learning enables attackers to tailor phishing emails that feel authentic and personally relevant, making phishing even more deceptive.
Exploit Development: AI-driven tools assist in uncovering zero-day vulnerabilities by automating and refining traditional techniques like fuzzing, which involves bombarding software with random inputs to expose weaknesses.
Malware Creation: Machine learning algorithms can make malware more evasive by adapting to the target’s security measures in real time, allowing it to slip through defenses.
Automated Reconnaissance: Hackers use AI to analyze massive data sets, such as social media profiles or organizational networks, to find weak points and personalize attacks.
Credential Stuffing and Brute Force: AI speeds up credential-stuffing attacks by automating the testing of large sets of stolen credentials against a variety of online platforms.
Deepfake Phishing: AI-generated audio and video deepfakes can impersonate trusted individuals, making social engineering attacks more convincing and difficult to detect.

For more detail on these evolving threats, you can read the full article on CSO Online.

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: Machine Learning

Comments (9)

Oct 11 2024

To fight AI-generated malware, focus on cybersecurity fundamentals

Category: AI — disc7 @ 8:08 am

AI-powered malware is increasingly adopting AI capabilities to improve traditional cyberattack techniques. Malware such as BlackMamba and EyeSpy leverage AI for activities like evading detection and conducting more sophisticated phishing attacks. These innovations are not entirely new but represent a refinement of existing malware strategies.

While AI enhances these attacks, its greatest danger lies in the automation of simple, widespread threats, potentially increasing the volume of attacks. To combat this, businesses need strong cybersecurity practices, including regular updates, training, and the integration of AI in defense systems for faster threat detection and response.

As with the future of AI-powered threats, AI’s impact on cybersecurity practitioners is likely to be more of a gradual change than an explosive upheaval. Rather than getting swept up in the hype or carried away by the doomsayers, security teams are better off doing what they’ve always done: keeping an eye on the future with both feet planted firmly in the present.

For more details, visit the IBM article.

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

ChatGPT for Cybersecurity Cookbook: Learn practical generative AI recipes to supercharge your cybersecurity skills

Previous DISC InfoSec posts on AI

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: Adversarial AI Attacks, AI-generated malware, ChatGPT for Cybersecurity

Comments (7)

Oct 04 2024

4 ways AI is transforming audit, risk and compliance

Category: AI,Risk Assessment,Security Compliance — disc7 @ 9:11 am

AI is revolutionizing audit, risk, and compliance by streamlining processes through automation. Tasks like data collection, control testing, and risk assessments, which were once time-consuming, are now being done faster and with more precision. This allows teams to focus on more critical strategic decisions.

In auditing, AI identifies anomalies and uncovers patterns in real-time, enhancing both the depth and accuracy of audits. AI’s ability to process large datasets also helps maintain compliance with evolving regulations like the EU’s AI Act, while mitigating human error.

Beyond audits, AI supports risk management by providing dynamic insights that adapt to changing threat landscapes. This enables continuous risk monitoring rather than periodic reviews, making organizations more responsive to emerging risks, including cybersecurity threats.

AI also plays a crucial role in bridging the gap between cybersecurity, compliance, and ESG (Environmental, Social, Governance) goals. It integrates these areas into a single strategy, allowing businesses to track and manage risks while aligning with sustainability initiatives and regulatory requirements.

For more details, visit here

AI Security risk assessment quiz

Trust Me – AI Risk Management

AI Management System Certification According to the ISO/IEC 42001 Standard

Responsible AI in the Enterprise: Practical AI risk management for explainable, auditable, and safe models with hyperscalers and Azure OpenAI

Previous posts on AI

Implementing BS ISO/IEC 42001 will demonstrate that you’re developing AI responsibly

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: AI audit, AI compliance, AI risk assessment, AI Risk Management

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

Oct 03 2024

AI security bubble already springing leaks

Category: AI — disc7 @ 1:17 pm

AI security bubble already springing leaks

The article highlights how the AI boom, especially in cybersecurity, is already showing signs of strain. Many AI startups, despite initial hype, are facing financial challenges, as they lack the funds to develop large language models (LLMs) independently. Larger companies are taking advantage by acquiring or licensing the technologies from these smaller firms at a bargain.

AI is just one piece of the broader cybersecurity puzzle, but it isn’t a silver bullet. Issues like system updates and cloud vulnerabilities remain critical, and AI-only security solutions may struggle without more comprehensive approaches.

Some efforts to set benchmarks for LLMs, like NIST, are underway, helping to establish standards in areas such as automated exploits and offensive security. However, AI startups face increasing difficulty competing with big players who have the resources to scale.

For more information, you can visit here

Implementing BS ISO/IEC 42001 will demonstrate that you’re developing AI responsibly

Previous posts on AI

AI Security risk assessment quiz

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: Adversarial AI Attacks, AI security

Comments (4)

Oct 01 2024

Could APIs be the undoing of AI?

Category: AI,API security — disc7 @ 11:32 am

The article discusses security challenges associated with large language models (LLMs) and APIs, focusing on issues like prompt injection, data leakage, and model theft. It highlights vulnerabilities identified by OWASP, including insecure output handling and denial-of-service attacks. API flaws can expose sensitive data or allow unauthorized access. To mitigate these risks, it recommends implementing robust access controls, API rate limits, and runtime monitoring, while noting the need for better protections against AI-based attacks.

The post discusses defense strategies against attacks targeting large language models (LLMs). Providers are red-teaming systems to identify vulnerabilities, but this alone isn’t enough. It emphasizes the importance of monitoring API activity to prevent data exposure and defend against business logic abuse. Model theft (LLMjacking) is highlighted as a growing concern, where attackers exploit cloud-hosted LLMs for profit. Organizations must act swiftly to secure LLMs and avoid relying solely on third-party tools for protection.

For more details, visit Help Net Security.

Hacking APIs: Breaking Web Application Programming Interfaces

Trust Me – AI Risk Management

AI Security risk assessment quiz

Implementing BS ISO/IEC 42001 will demonstrate that you’re developing AI responsibly

InfoSec services | InfoSec books | Follow our blog | DISC llc is listed on The vCISO Directory | ISO 27k Chat bot

Tags: AI, AI Risk Management, API security risks, Hacking APIs