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AI risk management and governance, so aligning your risk management policy means integrating AI-specific considerations alongside your existing risk framework. Here’s a structured approach:
1. Understand ISO 42001 Scope and Requirements
ISO 42001 sets standards for AI governance, risk management, and compliance across the AI lifecycle.
Key areas include:
Risk identification and assessment for AI systems.
Mitigation strategies for bias, errors, security, and ethical concerns.
Transparency, explainability, and accountability of AI models.
Compliance with legal and regulatory requirements (GDPR, EU AI Act, etc.).
2. Map Your Current Risk Policy
Identify where your existing policy addresses:
Risk assessment methodology
Roles and responsibilities
Monitoring and reporting
Incident response and corrective actions
Note gaps related to AI-specific risks, such as algorithmic bias, model explainability, or data provenance.
3. Integrate AI-Specific Risk Controls
AI Risk Identification: Add controls for data quality, model performance, and potential bias.
Risk Assessment: Include likelihood, impact, and regulatory consequences of AI failures.
Mitigation Strategies: Document methods like model testing, monitoring, human-in-the-loop review, or bias audits.
Governance & Accountability: Assign clear ownership for AI system oversight and compliance reporting.
4. Ensure Regulatory and Ethical Alignment
Map your AI systems against applicable standards:
EU AI Act (high-risk AI systems)
GDPR or HIPAA for data privacy
ISO 31000 for general risk management principles
Document how your policy addresses ethical AI principles, including fairness, transparency, and accountability.
5. Update Policy Language and Procedures
Add a dedicated “AI Risk Management” section to your policy.
Include:
Scope of AI systems covered
Risk assessment processes
Monitoring and reporting requirements
Training and awareness for stakeholders
Ensure alignment with ISO 42001 clauses (risk identification, evaluation, mitigation, monitoring).
6. Implement Monitoring and Continuous Improvement
Establish KPIs and metrics for AI risk monitoring.
Include regular audits and reviews to ensure AI systems remain compliant.
Integrate lessons learned into updates of the policy and risk register.
7. Documentation and Evidence
Keep records of:
AI risk assessments
Mitigation plans
Compliance checks
Incident responses
This will support ISO 42001 certification or internal audits.
Karen Hao’s Empire of AI provides a critical lens on the current AI landscape, questioning what intelligence truly means in these systems. Hao explores how AI is often framed as an extraordinary form of intelligence, yet in reality, it remains highly dependent on the data it is trained on and the design choices of its creators.
She highlights the ways companies encourage users to adopt AI tools, not purely for utility, but to collect massive amounts of data that can later be monetized. This approach, she argues, blurs the line between technological progress and corporate profit motives.
According to Hao, the AI industry often distorts reality. She describes AI as overhyped, framing the movement almost as a quasi-religious phenomenon. This hype, she suggests, fuels unrealistic expectations both among developers and the public.
Within the AI discourse, two camps emerge: the “boomers” and the “doomers.” Boomers herald AI as a new form of superior intelligence that can solve all problems, while doomers warn that this same intelligence could ultimately be catastrophic. Both, Hao argues, exaggerate what AI can actually do.
Prominent figures sometimes claim that AI possesses “PhD-level” intelligence, capable of performing complex, expert-level tasks. In practice, AI systems often succeed or fail depending on the quality of the data they consume—a vulnerability when that data includes errors or misinformation.
Hao emphasizes that the hype around AI is driven by money and venture capital, not by a transformation of the economy. According to her, Silicon Valley’s culture thrives on exaggeration: bigger models, more data, and larger data centers are marketed as revolutionary, but these features alone do not guarantee real-world impact.
She also notes that technology is not omnipotent. AI is not independently replacing jobs; company executives make staffing decisions. As people recognize the limits of AI, they can make more informed, “intelligent” choices themselves, countering some of the fears and promises surrounding automation.
OpenAI exemplifies these tensions. Founded as a nonprofit intended to counter Silicon Valley’s profit-driven AI development, it quickly pivoted toward a capitalistic model. Today, OpenAI is valued around $300–400 billion, and its focus is on data and computing power rather than purely public benefit, reflecting the broader financial incentives in the AI ecosystem.
Hao likens the AI industry to 18th-century colonialism: labor exploitation, monopolization of energy resources, and accumulation of knowledge and talent in wealthier nations echo historical imperial practices. This highlights that AI’s growth has social, economic, and ethical consequences far beyond mere technological achievement.
Hao’s analysis shows that AI, while powerful, is far from omnipotent. The overhype and marketing-driven narrative can weaken society by creating unrealistic expectations, concentrating wealth and power in the hands of a few corporations, and masking the social and ethical costs of these technologies. Instead of empowering people, it can distort labor markets, erode worker rights, and foster dependence on systems whose decision-making processes are opaque. A society that uncritically embraces AI risks being shaped more by financial incentives than by human-centered needs.
Today’s AI can perform impressive feats—from coding and creating images to diagnosing diseases and simulating human conversation. While these capabilities offer huge benefits, AI could be misused, from autonomous weapons to tools that spread misinformation and destabilize societies. Experts like Elon Musk and Geoffrey Hinton echo these concerns, advocating for regulations to keep AI safely under human control.
Unlock the power of AI and data with confidence through DISC InfoSec Group’s AI Security Risk Assessment and ISO 42001 AI Governance solutions. In today’s digital economy, data is your most valuable asset and AI the driver of innovation — but without strong governance, they can quickly turn into liabilities. We help you build trust and safeguard growth with robust Data Governance and AI Governance frameworks that ensure compliance, mitigate risks, and strengthen integrity across your organization. From securing data with ISO 27001, GDPR, and HIPAA to designing ethical, transparent AI systems aligned with ISO 42001, DISC InfoSec Group is your trusted partner in turning responsibility into a competitive advantage. Govern your data. Govern your AI. Secure your future.
Ready to build a smarter, safer future? When Data Governance and AI Governance work in harmony, your organization becomes more agile, compliant, and trusted. At Deura InfoSec Group, we help you lead with confidence by aligning governance with business goals — ensuring your growth is powered by trust, not risk. Schedule a consultation today and take the first step toward building a secure future on a foundation of responsibility.
The strategic synergy between ISO/IEC 27001 and ISO/IEC 42001 marks a new era in governance. While ISO 27001 focuses on information security — safeguarding data confidentiality, integrity, and availability — ISO 42001 is the first global standard for governing AI systems responsibly. Together, they form a powerful framework that addresses both the protection of information and the ethical, transparent, and accountable use of AI.
Organizations adopting AI cannot rely solely on traditional information security controls. ISO 42001 brings in critical considerations such as AI-specific risks, fairness, human oversight, and transparency. By integrating these governance frameworks, you ensure not just compliance, but also responsible innovation — where security, ethics, and trust work together to drive sustainable success.
Building trustworthy AI starts with high-quality, well-governed data. At Deura InfoSec Group, we ensure your AI systems are designed with precision — from sourcing and cleaning data to monitoring bias and validating context. By aligning with global standards like ISO/IEC 42001 and ISO/IEC 27001, we help you establish structured practices that guarantee your AI outputs are accurate, reliable, and compliant. With strong data governance frameworks, you minimize risk, strengthen accountability, and build a foundation for ethical AI.
Whether your systems rely on training data or testing data, our approach ensures every dataset is reliable, representative, and context-aware. We guide you in handling sensitive data responsibly, documenting decisions for full accountability, and applying safeguards to protect privacy and security. The result? AI systems that inspire confidence, deliver consistent value, and meet the highest ethical and regulatory standards. Trust Deura InfoSec Group to turn your data into a strategic asset — powering safe, fair, and future-ready AI.
ISO 42001-2023 Control Gap Assessment
Unlock the competitive edge with ourISO 42001:2023 Control Gap Assessment— the fastest way to measure your organization’s readiness for responsible AI. This assessment identifies gaps between your current practices and the world’s first international AI governance standard, giving you a clear roadmap to compliance, risk reduction, and ethical AI adoption.
By uncovering hidden risks such as bias, lack of transparency, or weak oversight, our gap assessment helps you strengthen trust, meet regulatory expectations, and accelerate safe AI deployment. The outcome: a tailored action plan that not only protects your business from costly mistakes but also positions you as a leader in responsible innovation. With DISC InfoSec Group, you don’t just check a box — you gain a strategic advantage built on integrity, compliance, and future-proof AI governance.
ISO 27001 will always be vital, but it’s no longer sufficient by itself. True resilience comes from combining ISO 27001’s security framework withISO 42001’s AI governance, delivering a unified approach to risk and compliance. This evolution goes beyond an upgrade — it’s a transformative shift in how digital trust is established and protected.
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This proactive approach, which we call Proactive compliance, distinguishes our clients in regulated sectors.
For AI at scale, the real question isn’t “Can we comply?” but “Can we design trust into the system from the start?”
Visit our site today and discover how we can help you lead with responsible AI governance.
1. Framing a Risk-Aware AI Strategy The book begins by laying out the need for organizations to approach AI not just as a source of opportunity (innovation, efficiency, etc.) but also as a domain rife with risk: ethical risks (bias, fairness), safety, transparency, privacy, regulatory exposure, reputational risk, and so on. It argues that a risk-aware strategy must be integrated into the whole AI lifecycle—from design to deployment and maintenance. Key in its framing is that risk management shouldn’t be an afterthought or a compliance exercise; it should be embedded in strategy, culture, governance structures. The idea is to shift from reactive to proactive: anticipating what could go wrong, and building in mitigations early.
2. How the book leverages ISO 42001 and related standards A core feature of the book is that it aligns its framework heavily with ISO IEC 42001:2023, which is the first international standard to define requirements for establishing, implementing, maintaining, and continuously improving an Artificial Intelligence Management System (AIMS). The book draws connections between 42001 and adjacent or overlapping standards—such as ISO 27001 (information security), ISO 31000 (risk management in general), as well as NIST’s AI Risk Management Framework (AI RMF 1.0). The treatment helps the reader see how these standards can interoperate—where one handles confidentiality, security, access controls (ISO 27001), another handles overall risk governance, etc.—and how 42001 fills gaps specific to AI: lifecycle governance, transparency, ethics, stakeholder traceability.
3. The Artificial Intelligence Management System (AIMS) as central tool The concept of an AI Management System (AIMS) is at the heart of the book. An AIMS per ISO 42001 is a set of interrelated or interacting elements of an organization (policies, controls, processes, roles, tools) intended to ensure responsible development and use of AI systems. The author Andrew Pattison walks through what components are essential: leadership commitment; roles and responsibilities; risk identification, impact assessment; operational controls; monitoring, performance evaluation; continual improvement. One strength is the practical guidance: not just “you should do these”, but how to embed them in organizations that don’t have deep AI maturity yet. The book emphasizes that an AIMS is more than a set of policies—it’s a living system that must adapt, learn, and respond as AI systems evolve, as new risks emerge, and as external demands (laws, regulations, public expectations) shift.
4. Comparison and contrasts: ISO 42001, ISO 27001, and NIST In comparing standards, the book does a good job of pointing out both overlaps and distinct value: for example, ISO 27001 is strong on information security, confidentiality, integrity, availability; it has proven structures for risk assessment and for ensuring controls. But AI systems pose additional, unique risks (bias, accountability of decision-making, transparency, possible harms in deployment) that are not fully covered by a pure security standard. NIST’s AI Risk Management Framework provides flexible guidance especially for U.S. organisations or those aligning with U.S. governmental expectations: mapping, measuring, managing risks in a more domain-agnostic way. Meanwhile, ISO 42001 brings in the notion of an AI-specific management system, lifecycle oversight, and explicit ethical / governance obligations. The book argues that a robust strategy often uses multiple standards: e.g. ISO 27001 for information security, ISO 42001 for overall AI governance, NIST AI RMF for risk measurement & tools.
5. Practical tools, governance, and processes The author does more than theory. There are discussions of impact assessments, risk matrices, audit / assurance, third-party oversight, monitoring for model drift / unanticipated behavior, documentation, and transparency. Some of the more compelling content is about how to do risk assessments early (before deployment), how to engage stakeholders, how to map out potential harms (both known risks and emergent/unknown ones), how governance bodies (steering committees, ethics boards) can play a role, how responsibility should be assigned, how controls should be tested. The book does point out real challenges: culture change, resource constraints, measurement difficulties, especially for ethical or fairness concerns. But it provides guidance on how to surmount or mitigate those.
6. What might be less strong / gaps While the book is very useful, there are areas where some readers might want more. For instance, in scaling these practices in organizations with very little AI maturity: the resource costs, how to bootstrap without overengineering. Also, while it references standards and regulations broadly, there may be less depth on certain jurisdictional regulatory regimes (e.g. EU AI Act in detail, or sector-specific requirements). Another area that is always hard—and the book is no exception—is anticipating novel risks: what about very advanced AI systems (e.g. generative models, large language models) or AI in uncontrolled environments? Some of the guidance is still high-level when it comes to edge-cases or worst-case scenarios. But this is a natural trade-off given the speed of AI advancement.
7. Future of AI & risk management: trends and implications Looking ahead, the book suggests that risk management in AI will become increasingly central as both regulatory pressure and societal expectations grow. Standards like ISO 42001 will be adopted more widely, possibly even made mandatory or incorporated into regulation. The idea of “certification” or attestation of compliance will gain traction. Also, the monitoring, auditing, and accountability functions will become more technically and institutionally mature: better tools for algorithmic transparency, bias measurement, model explainability, data provenance, and impact assessments. There’ll also be more demand for cross-organizational cooperation (e.g. supply chains and third-party models), for oversight of external models, for AI governance in ecosystems rather than isolated systems. Finally, there is an implication that organizations that don’t get serious about risk will pay—through regulation, loss of trust, or harm. So the future is of AI risk management moving from “nice-to-have” to “mission-critical.”
Overall, Managing AI Risk is a strong, timely guide. It bridges theory (standards, frameworks) and practice (governance, processes, tools) well. It makes the case that ISO 42001 is a useful centerpiece for any AI risk strategy, especially when combined with other standards. If you are planning or refining an AI strategy, building or implementing an AIMS, or anticipating future regulatory change, this book gives a solid and actionable foundation.
Artificial Intelligence (AI) has transitioned from experimental to operational, driving transformations across healthcare, finance, education, transportation, and government. With its rapid adoption, organizations face mounting pressure to ensure AI systems are trustworthy, ethical, and compliant with evolving regulations such as the EU AI Act, Canada’s AI Directive, and emerging U.S. policies. Effective governance and risk management have become critical to mitigating potential harms and reputational damage.
ISO 42001 isn’t just an additional compliance framework—it serves as the integration layer that brings all AI governance, risk, control monitoring and compliance efforts together into a unified system called AIMS.
To address these challenges, a structured governance, risk, and compliance (GRC) framework is essential. ISO/IEC 42001:2023 – the Artificial Intelligence Management System (AIMS) standard – provides organizations with a comprehensive approach to managing AI responsibly, similar to how ISO/IEC 27001 supports information security.
ISO/IEC 42001 is the world’s first international standard specifically for AI management systems. It establishes a management system framework (Clauses 4–10) and detailed AI-specific controls (Annex A). These elements guide organizations in governing AI responsibly, assessing and mitigating risks, and demonstrating compliance to regulators, partners, and customers.
One of the key benefits of ISO/IEC 42001 is stronger AI governance. The standard defines leadership roles, responsibilities, and accountability structures for AI, alongside clear policies and ethical guidelines. By aligning AI initiatives with organizational strategy and stakeholder expectations, organizations build confidence among boards, regulators, and the public that AI is being managed responsibly.
ISO/IEC 42001 also provides a structured approach to risk management. It helps organizations identify, assess, and mitigate risks such as bias, lack of explainability, privacy issues, and safety concerns. Lifecycle controls covering data, models, and outputs integrate AI risk into enterprise-wide risk management, preventing operational, legal, and reputational harm from unintended AI consequences.
Compliance readiness is another critical benefit. ISO/IEC 42001 aligns with global regulations like the EU AI Act and OECD AI Principles, ensuring robust data quality, transparency, human oversight, and post-market monitoring. Internal audits and continuous improvement cycles create an audit-ready environment, demonstrating regulatory compliance and operational accountability.
Finally, ISO/IEC 42001 fosters trust and competitive advantage. Certification signals commitment to responsible AI, strengthening relationships with customers, investors, and regulators. For high-risk sectors such as healthcare, finance, transportation, and government, it provides market differentiation and reinforces brand reputation through proven accountability.
Opinion: ISO/IEC 42001 is rapidly becoming the foundational standard for responsible AI deployment. Organizations adopting it not only safeguard against risks and regulatory penalties but also position themselves as leaders in ethical, trustworthy AI system. For businesses serious about AI’s long-term impact, ethical compliance, transparency, user trust ISO/IEC 42001 is as essential as ISO/IEC 27001 is for information security.
Most importantly, ISO 42001 AIMS is built to integrate seamlessly with ISO 27001 ISMS. It’s highly recommended to first achieve certification or alignment with ISO 27001 before pursuing ISO 42001.
AIMS and Data Governance – Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative.
ISO 42001—the first international standard for managing artificial intelligence. Developed for organizations that design, deploy, or oversee AI, ISO 42001 is set to become the ISO 9001 of AI: a universal framework for trustworthy, transparent, and responsible AI.
“Whether you’re a technology professional, policymaker, academic, or simply a curious reader, this book will arm you with the knowledge to navigate the complex intersection of AI, security, and society.”
ISO 42001 is the upcoming standard for AI Management Systems (AIMS), similar in structure to ISO 27001 for information security. While the full standard is not yet widely published, the main requirements for an internal audit of an ISO 42001 AIMS can be outlined based on common audit principles and the expected clauses in the standard. Here’s a structured view:
1. Audit Scope and Objectives
Define what parts of the AI management system will be audited (processes, teams, AI models, AI governance, data handling, etc.).
Ensure the audit covers all ISO 42001 clauses relevant to your organization.
Determine audit objectives, e.g.,:
Compliance with ISO 42001.
Effectiveness of risk management for AI.
Alignment with organizational AI strategy and policies.
2. Compliance with AIMS Requirements
Check whether the organization’s AI management system meets ISO 42001 requirements, which likely include:
AI governance framework.
Risk management for AI (AI lifecycle, bias, safety, privacy).
Policies and procedures for AI development, deployment, and monitoring.
Data management and ethical AI principles.
Roles, responsibilities, and competency requirements for AI personnel.
3. Documentation and Records
Verify that documentation exists and is maintained, e.g.:
AI policies, procedures, and guidelines.
Risk assessments, impact assessments, and mitigation plans.
Training records and personnel competency evaluations.
Records of AI incidents, anomalies, or failures.
Audit logs of AI models and data handling activities.
4. Risk Management and Controls
Review whether risks related to AI (bias, safety, security, privacy) are identified, assessed, and mitigated.
Check implementation of controls:
Data quality and integrity controls.
Model validation and testing.
Human oversight and accountability mechanisms.
Compliance with relevant regulations and ethical standards.
5. Performance Monitoring and Improvement
Evaluate monitoring and measurement processes:
Metrics for AI model performance and compliance.
Monitoring of ethical and legal adherence.
Feedback loops for continuous improvement.
Assess whether corrective actions and improvements are identified and implemented.
6. Internal Audit Process Requirements
Audits should be planned, objective, and systematic.
Auditors must be independent of the area being audited.
Audit reports must include:
Findings (compliance, nonconformities, opportunities for improvement).
Recommendations.
Follow-up to verify closure of nonconformities.
7. Management Review Alignment
Internal audit results should feed into management reviews for:
AI risk mitigation effectiveness.
Resource allocation.
Policy updates and strategic AI decisions.
Key takeaway: An ISO 42001 internal audit is not just about checking boxes—it’s about verifying that AI systems are governed, ethical, and risk-managed throughout their lifecycle, with evidence, controls, and continuous improvement in place.
An Internal Audit agreement aligned with ISO 42001 should include the following key components, each described below to ensure clarity and operational relevance:
🧭 Scope of Services
The agreement should clearly define the consultant’s role in leading and advising the internal audit team. This includes directing the audit process, training team members on ISO 42001 methodologies, and overseeing all phases—from planning to reporting. It should also specify advisory responsibilities such as interpreting ISO 42001 requirements, identifying compliance gaps, and validating governance frameworks. The scope must emphasize the consultant’s authority to review and approve all audit work to ensure alignment with professional standards.
📄 Deliverables
A detailed list of expected outputs should be included, such as a comprehensive audit report with an executive summary, gap analysis, and risk assessment. The agreement should also cover a remediation plan with prioritized actions, implementation guidance, and success metrics. Supporting materials like policy templates, training recommendations, and compliance monitoring frameworks should be outlined. Finally, it should ensure the development of a capable internal audit team and documentation of audit procedures for future use.
⏳ Timeline
The agreement must specify key milestones, including project start and completion dates, training deadlines, audit phase completion, and approval checkpoints for draft and final reports. This timeline ensures accountability and helps coordinate internal resources effectively.
💰 Compensation
This section should detail the total project fee, payment terms, and a milestone-based payment schedule. It should also clarify reimbursable expenses (e.g., travel) and note that internal team costs and facilities are the client’s responsibility. Transparency in financial terms helps prevent disputes and ensures mutual understanding.
👥 Client Responsibilities
The client’s obligations should be clearly stated, including assigning qualified internal audit team members, ensuring their availability, designating a project coordinator, and providing access to necessary personnel, systems, and facilities. The agreement should also require timely feedback on deliverables and commitment from the internal team to complete audit tasks under the consultant’s guidance.
🎓 Consultant Responsibilities
The consultant’s duties should include providing expert leadership, training the internal team, reviewing and approving all work products, maintaining quality standards, and being available for ongoing consultation. This ensures the consultant remains accountable for the integrity and effectiveness of the audit process.
🔐 Confidentiality
A robust confidentiality clause should protect proprietary information shared during the engagement. It should specify the duration of confidentiality obligations post-engagement and ensure that internal audit team members are bound by equivalent terms. This builds trust and safeguards sensitive data.
💡 Intellectual Property
The agreement should clarify ownership of work products, stating that outputs created by the internal team under the consultant’s guidance belong to the client. It should also allow the consultant to retain general methodologies and templates for future use, while jointly owning training materials and audit frameworks.
⚖️ Limitation of Liability
This clause should cap the consultant’s liability to the total fee paid and exclude consequential or punitive damages. It should reinforce that ISO 42001 compliance is ultimately the client’s responsibility, with the consultant providing guidance and oversight—not execution.
🛑 Termination
The agreement should include provisions for termination with advance notice, payment for completed work, delivery of all completed outputs, and survival of confidentiality obligations. It should also ensure that any training and knowledge transfer remains with the client post-termination.
📜 General Terms
Standard legal provisions should be included, such as independent contractor status, governing law, severability, and a clause stating that the agreement represents the entire understanding between parties. These terms provide legal clarity and protect both sides.
Cybersecurity is no longer confined to the IT department — it has become a fundamental issue of business survival. The past year has shown that security failures don’t just disrupt operations; they directly impact reputation, financial stability, and customer trust. Organizations that continue to treat it as a back-office function risk being left exposed.
Over the last twelve months, we’ve seen high-profile companies fined millions of dollars for data breaches. These penalties demonstrate that regulators and customers alike are holding businesses accountable for their ability to protect sensitive information. The cost of non-compliance now goes far beyond the technical cleanup — it threatens long-term credibility.
Another worrying trend has been the exploitation of supply chain partners. Attackers increasingly target smaller vendors with weaker defenses to gain access to larger organizations. This highlights that cybersecurity is no longer contained within one company’s walls; it is interconnected, making vendor oversight and third-party risk management critical.
Adding to the challenge is the rapid adoption of artificial intelligence. While AI brings efficiency and innovation, it also introduces untested and often misunderstood risks. From data poisoning to model manipulation, organizations are entering unfamiliar territory, and traditional controls don’t always apply.
Despite these evolving threats, many businesses continue to frame the wrong question: “Do we need certification?” While certification has its value, it misses the bigger picture. The right question is: “How do we protect our data, our clients, and our reputation — and demonstrate that commitment clearly?” This shift in perspective is essential to building a sustainable security culture.
This is where frameworks such as ISO 27001, ISO 27701, and ISO 42001 play a vital role. They are not merely compliance checklists; they provide structured, internationally recognized approaches for managing security, privacy, and AI governance. Implemented correctly, these frameworks become powerful tools to build customer trust and show measurable accountability.
Every organization faces its own barriers in advancing security and compliance. For some, it’s budget constraints; for others, it’s lack of leadership buy-in or a shortage of skilled professionals. Recognizing and addressing these obstacles early is key to moving forward. Without tackling them, even the best frameworks will sit unused, failing to provide real protection.
My advice: Stop viewing cybersecurity as a cost center or certification exercise. Instead, approach it as a business enabler — one that safeguards reputation, strengthens client relationships, and opens doors to new opportunities. Begin by identifying your organization’s greatest barrier, then create a roadmap that aligns frameworks with business goals. When leadership sees cybersecurity as an investment in trust, adoption becomes much easier and far more impactful.
Continual improvement doesn’t necessarily entail significant expenses. Many enhancements can be achieved through regular internal audits, management reviews, and staff engagement. By fostering a culture of continuous improvement, organizations can maintain an ISMS that effectively addresses current and emerging information security risks, ensuring resilience and compliance with ISO 27001 standards.
At DISC InfoSec, we streamline the entire process—guiding you confidently through complex frameworks such as ISO 27001, and SOC 2.
Here’s how we help:
Conduct gap assessments to identify compliance challenges and control maturity
Deliver straightforward, practical steps for remediation with assigned responsibility
Ensure ongoing guidance to support continued compliance with standard
Confirm your security posture through risk assessments and penetration testing
Let’s set up a quick call to explore how we can make your cybersecurity compliance process easier.
ISO 27001 certification validates that your ISMS meets recognized security standards and builds trust with customers by demonstrating a strong commitment to protecting information.
Feel free to get in touch if you have any questions about the ISO 27001, ISO 42001, ISO 27701 Internal audit or certification process.
Successfully completing your ISO 27001 audit confirms that your Information Security Management System (ISMS) meets the required standards and assures your customers of your commitment to security.
Get in touch with us to begin your ISO 27001 audit today.
The ISO/IEC 42001 standard and the NIST AI Risk Management Framework (AI RMF) are two cornerstone tools for businesses aiming to ensure the responsible development and use of AI. While they differ in structure and origin, they complement each other beautifully. Here’s a breakdown of how each contributes—and how they align.
🧭 ISO/IEC 42001: AI Management System Standard
Purpose: Establishes a formal AI Management System (AIMS) across the organization, similar to ISO 27001 for information security.
🔧 Key Components
Leadership & Governance: Requires executive commitment and clear accountability for AI risks.
Policy & Planning: Organizations must define AI objectives, ethical principles, and risk tolerance.
Operational Controls: Covers data governance, model lifecycle management, and supplier oversight.
Monitoring & Improvement: Includes performance evaluation, impact assessments, and continuous improvement loops.
✅ Benefits
Embeds responsibility and accountability into every phase of AI development.
Supports legal compliance with regulations like the EU AI Act and GDPR.
Enables certification, signaling trustworthiness to clients and regulators.
🧠 NIST AI Risk Management Framework (AI RMF)
Purpose: Provides a flexible, voluntary framework for identifying, assessing, and managing AI risks.
🧩 Core Functions
Function
Description
Govern
Establish organizational policies and accountability for AI risks
Map
Understand the context, purpose, and stakeholders of AI systems
Measure
Evaluate risks, including bias, robustness, and explainability
Manage
Implement controls and monitor performance over time
✅ Benefits
Promotes trustworthy AI through transparency, fairness, and safety.
Helps organizations operationalize ethical principles without requiring certification.
Adaptable across industries and AI maturity levels.
🔗 How They Work Together
ISO/IEC 42001
NIST AI RMF
Formal, certifiable management system
Flexible, voluntary risk management framework
Focus on organizational governance
Focus on system-level risk controls
PDCA cycle for continuous improvement
Iterative risk assessment and mitigation
Strong alignment with EU AI Act compliance
Strong alignment with U.S. Executive Order on AI
Together, they offer a dual lens:
ISO 42001 ensures enterprise-wide governance and accountability.
NIST AI RMF ensures system-level risk awareness and mitigation.
visual comparison chart or a mind map to show how these frameworks align with the EU AI Act or sector-specific obligations.
mind map comparing ISO/IEC 42001 and the NIST AI RMF for responsible AI development and use:
This visual lays out the complementary roles of each framework:
ISO/IEC 42001 focuses on building an enterprise-wide AI management system with governance, accountability, and operational controls.
NIST AI RMF zeroes in on system-level risk identification, assessment, and mitigation.
1. The New Era of AI Governance AI is now part of everyday life—from facial recognition and recommendation engines to complex decision-making systems. As AI capabilities multiply, businesses urgently need standardized frameworks to manage associated risks responsibly. ISO 42001:2023, released at the end of 2023, offers the first global management system standard dedicated entirely to AI systems.
2. What ISO 42001 Offers The standard establishes requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS). It covers everything from ethical use and bias mitigation to transparency, accountability, and data governance across the AI lifecycle.
3. Structure and Risk-Based Approach Built around the Plan-Do-Check-Act (PDCA) methodology, ISO 42001 guides organizations through formal policies, impact assessments, and continuous improvement cycles—mirroring the structure used by established ISO standards like ISO 27001. However, it is tailored specifically for AI management needs.
4. Core Benefits of Adoption Implementing ISO 42001 helps organizations manage AI risks effectively while demonstrating responsible and transparent AI governance. Benefits include decreased bias, improved user trust, operational efficiency, and regulatory readiness—particularly relevant as AI legislation spreads globally.
5. Complementing Existing Standards ISO 42001 can integrate with other management systems such as ISO 27001 (information security) or ISO 27701 (privacy). Organizations already certified to other standards can adapt existing controls and processes to meet new AI-specific requirements, reducing implementation effort.
6. Governance Across AI Lifecycle The standard covers every stage of AI—from development and deployment to decommissioning. Key controls include leadership and policy setting, risk and impact assessments, transparency, human oversight, and ongoing monitoring of performance and fairness.
7. Certification Process Overview Certification follows the familiar ISO 17021 process: a readiness assessment, then stage 1 and stage 2 audits. Once certified, organizations remain valid for three years, with annual surveillance audits to ensure ongoing adherence to ISO 42001 clauses and controls.
8. Market Trends and Regulatory Context Interest in ISO 42001 is rising quickly in 2025, driven by global AI regulation like the EU AI Act. While certification remains voluntary, organizations adopting it gain competitive advantage and pre-empt regulatory obligations.
9. Controls Aligned to Ethical AI ISO 42001 includes 38 distinct controls grouped into control objectives addressing bias mitigation, data quality, explainability, security, and accountability. These facilitate ethical AI while aligning with both organizational and global regulatory expectations.
10. Forward-Looking Compliance Strategy Though certification may become more common in 2026 and beyond, organizations should begin early. Even without formal certification, adopting ISO 42001 practices enables stronger AI oversight, builds stakeholder trust, and sets alignment with emerging laws like the EU AI Act and evolving global norms.
Opinion: ISO 42001 establishes a much-needed framework for responsible AI management. It balances innovation with ethics, governance, and regulatory alignment—something no other AI-focused standard has fully delivered. Organizations that get ahead by building their AI governance around ISO 42001 will not only manage risk better but also earn stakeholder trust and future-proof against incoming regulations. With AI accelerating, ISO 42001 is becoming a strategic imperative—not just a nice-to-have.
The AICM (AI Controls Matrix) is a cybersecurity and risk management framework developed by the Cloud Security Alliance (CSA) to help organizations manage AI-specific risks across the AI lifecycle.
AICM stands for AI Controls Matrix, and it is:
A risk and control framework tailored for Artificial Intelligence (AI) systems.
Built to address trustworthiness, safety, and compliance in the design, development, and deployment of AI.
Structured across 18 security domains with 243 control objectives.
Aligned with existing standards like:
ISO/IEC 42001 (AI Management Systems)
ISO/IEC 27001
NIST AI Risk Management Framework
BSI AIC4
EU AI Act
+———————————————————————————+ | ARTIFICIAL INTELLIGENCE CONTROL MATRIX (AICM) | | 243 Control Objectives | 18 Security Domains | +———————————————————————————+
Domain No.
Domain Name
Example Controls Count
1
Governance & Leadership
15
2
Risk Management
14
3
Compliance & Legal
13
4
AI Ethics & Responsible AI
18
5
Data Governance
16
6
Model Lifecycle Management
17
7
Privacy & Data Protection
15
8
Security Architecture
13
9
Secure Development Practices
15
10
Threat Detection & Response
12
11
Monitoring & Logging
12
12
Access Control
14
13
Supply Chain Security
13
14
Business Continuity & Resilience
12
15
Human Factors & Awareness
14
16
Incident Management
14
17
Performance & Explainability
13
18
Third-Party Risk Management
13
+———————————————————————————+
TOTAL CONTROL OBJECTIVES: 243
+———————————————————————————+
Legend: 📘 = Policy Control 🔧 = Technical Control 🧠 = Human/Process Control 🛡️ = Risk/Compliance Control
🧩 Key Features
Covers traditional cybersecurity and AI-specific threats (e.g., model poisoning, data leakage, prompt injection).
Applies across the entire AI lifecycle—from data ingestion and training to deployment and monitoring.
Includes a companion tool: the AI-CAIQ (Consensus Assessment Initiative Questionnaire for AI), enabling organizations to self-assess or vendor-assess against AICM controls.
🎯 Why It Matters
As AI becomes pervasive in business, compliance, and critical infrastructure, traditional frameworks (like ISO 27001 alone) are no longer enough. AICM helps organizations:
Implement responsible AI governance
Identify and mitigate AI-specific security risks
Align with upcoming global regulations (like the EU AI Act)
Demonstrate AI trustworthiness to customers, auditors, and regulators
Here are the 18 security domains covered by the AICM framework:
Audit and Assurance
Application and Interface Security
Business Continuity Management and Operational Resilience
Supply Chain Management, Transparency and Accountability
Threat & Vulnerability Management
Universal Endpoint Management
Gap Analysis Template based on AICM (Artificial Intelligence Control Matrix)
#
Domain
Control Objective
Current State (1-5)
Target State (1-5)
Gap
Responsible
Evidence/Notes
Remediation Action
Due Date
1
Governance & Leadership
AI governance structure is formally defined.
2
5
3
John D.
No documented AI policy
Draft governance charter
2025-08-01
2
Risk Management
AI risk taxonomy is established and used.
3
4
1
Priya M.
Partial mapping
Align with ISO 23894
2025-07-25
3
Privacy & Data Protection
AI models trained on PII have privacy controls.
1
5
4
Sarah W.
Privacy review not performed
Conduct DPIA
2025-08-10
4
AI Ethics & Responsible AI
AI systems are evaluated for bias and fairness.
2
5
3
Ethics Board
Informal process only
Implement AI fairness tools
2025-08-15
…
…
…
…
…
…
…
…
…
…
🔢 Scoring Scale (Current & Target State)
1 – Not Implemented
2 – Partially Implemented
3 – Implemented but Not Reviewed
4 – Implemented and Reviewed
5 – Optimized and Continuously Improved
The AICM contains 243 control objectives distributed across 18 security domains, analyzed by five critical pillars, including Control Type, Control Applicability and Ownership, Architectural Relevance, LLM Lifecycle Relevance, and Threat Category.
It maps to leading standards, including NIST AI RMF 1.0 (via AI NIST 600-1), and BSI AIC4 (included today), as well as ISO 42001 & ISO 27001 (next month).
This will be the framework for STAR for AI organizational certification program. Any AI model provider, cloud service provider or SaaS provider will want to go through this program. CSA is leaving it open as to enterprises, they believe it is going to make sense for them to consider the certification as well. The release includes the Consensus Assessment Initiative Questionnaire for AI (AI-CAIQ), so CSA encourage you to start thinking about showing your alignment with AICM soon.
CSA will also adapt our Valid-AI-ted AI-based automated scoring tool to analyze AI-CAIQ submissions
Integrating ISO standards across business functions—particularly Governance, Risk, and Compliance (GRC)—has become not just a best practice but a necessity in the age of Artificial Intelligence (AI). As AI systems increasingly permeate operations, decision-making, and customer interactions, the need for standardized controls, accountability, and risk mitigation is more urgent than ever. ISO standards provide a globally recognized framework that ensures consistency, security, quality, and transparency in how organizations adopt and manage AI technologies.
In the GRC domain, ISO standards like ISO/IEC 27001 (information security), ISO/IEC 38500 (IT governance), ISO 31000 (risk management), and ISO/IEC 42001 (AI management systems) offer a structured approach to managing risks associated with AI. These frameworks guide organizations in aligning AI use with regulatory compliance, internal controls, and ethical use of data. For example, ISO 27001 helps in safeguarding data fed into machine learning models, while ISO 31000 aids in assessing emerging AI risks such as bias, algorithmic opacity, or unintended consequences.
The integration of ISO standards helps unify siloed departments—such as IT, legal, HR, and operations—by establishing a common language and baseline for risk and control. This cohesion is particularly crucial when AI is used across multiple departments. AI doesn’t respect organizational boundaries, and its risks ripple across all functions. Without standardized governance structures, businesses risk deploying fragmented, inconsistent, and potentially harmful AI systems.
ISO standards also support transparency and accountability in AI deployment. As regulators worldwide introduce new AI regulations—such as the EU AI Act—standards like ISO/IEC 42001 help organizations demonstrate compliance, build trust with stakeholders, and prepare for audits. This is especially important in industries like healthcare, finance, and defense, where the margin for error is small and ethical accountability is critical.
Moreover, standards-driven integration supports scalability. As AI initiatives grow from isolated pilot projects to enterprise-wide deployments, ISO frameworks help maintain quality and control at scale. ISO 9001, for instance, ensures continuous improvement in AI-supported processes, while ISO/IEC 27017 and 27018 address cloud security and data privacy—key concerns for AI systems operating in the cloud.
AI systems also introduce new third-party and supply chain risks. ISO standards such as ISO/IEC 27036 help in managing vendor security, and when integrated into GRC workflows, they ensure AI solutions procured externally adhere to the same governance rigor as internal developments. This is vital in preventing issues like AI-driven data breaches or compliance gaps due to poorly vetted partners.
Importantly, ISO integration fosters a culture of risk-aware innovation. Instead of slowing down AI adoption, standards provide guardrails that enable responsible experimentation and faster time to trust. They help organizations embed privacy, ethics, and accountability into AI from the design phase, rather than retrofitting compliance after deployment.
In conclusion, ISO standards are no longer optional checkboxes; they are strategic enablers in the age of AI. For GRC leaders, integrating these standards across business functions ensures that AI is not only powerful and efficient but also safe, transparent, and aligned with organizational values. As AI’s influence grows, ISO-based governance will distinguish mature, trusted enterprises from reckless adopters.
What does BS ISO/IEC 42001 – Artificial intelligence management system cover? BS ISO/IEC 42001:2023 specifies requirements and provides guidance for establishing, implementing, maintaining and continually improving an AI management system within the context of an organization.
ISO/IEC 42001:2023 – from establishing to maintain an AI management system.
ISO/IEC 27701 2019 Standard – Published in August of 2019, ISO 27701 is a new standard for information and data privacy. Your organization can benefit from integrating ISO 27701 with your existing security management system as doing so can help you comply with GDPR standards and improve your data security.
In the race to leverage artificial intelligence (AI), organizations are rushing to train, deploy, and scale AI systems—but often without fully addressing a critical piece of the puzzle: AI data security. The recent guidance from the Cybersecurity and Infrastructure Security Agency (CISA) and Cybersecurity Strategic Initiative (CSI) offers a timely blueprint for protecting AI-related data across its lifecycle.
Why AI Security Starts with Data
AI models are only as trustworthy as the data they are trained on. From sensitive customer information to proprietary business insights, the datasets feeding AI systems are now prime targets for attackers. That’s why the CSI emphasizes securing this data not just at rest or in transit, but throughout its entire lifecycle—from ingestion and training to inference and long-term storage.
A Lifecycle Approach to Risk
Traditional cybersecurity approaches aren’t enough. The AI lifecycle introduces new risks at every stage—like data poisoning during training or model inversion attacks during inference. To counter this, security leaders must adopt a holistic, lifecycle-based strategy that extends existing security controls into AI environments.
Know Your Data: Visibility and Classification
Effective AI security begins with understanding what data you have and where it lives. CSI guidance urges organizations to implement robust data discovery, labeling, and classification practices. Without this foundation, it’s nearly impossible to apply appropriate controls, meet regulatory requirements, or detect misuse.
Evolving Controls: IAM, Encryption, and Monitoring
It’s not just about locking data down. Security controls must evolve to fit AI workflows. This includes applying least privilege access, enforcing strong encryption, and continuously monitoring model behavior. CSI makes it clear: your developers and data scientists need tailored IAM policies, not generic access.
Model Integrity and Data Provenance
The source and quality of your data directly impact the trustworthiness of your AI. Tracking data provenance—knowing where it came from, how it was processed, and how it’s used—is essential for both compliance and model integrity. As new AI governance frameworks like ISO/IEC 42001 and NIST AI RMF gain traction, this capability will be indispensable.
Defending Against AI-Specific Threats
AI brings new risks that conventional tools don’t fully address. Model inversion, adversarial attacks, and data leakage are becoming common. CSI recommends implementing defenses like differential privacy, watermarking, and adversarial testing to reduce exposure—especially in sectors dealing with personal or regulated data.
Aligning Security and Strategy
Ultimately, protecting AI data is more than a technical issue—it’s a strategic one. CSI emphasizes the need for cross-functional collaboration between security, compliance, legal, and AI teams. By embedding security from day one, organizations can reduce risk, build trust, and unlock the true value of AI—safely.
Ready to Apply CSI Guidance to Your AI Roadmap?
Don’t leave your AI initiatives exposed to unnecessary risk. Whether you’re training models on sensitive data or deploying AI in regulated environments, now is the time to embed security across the lifecycle.
At Deura InfoSec, we help organizations translate CSI and CISA guidance into practical, actionable steps—from risk assessments and data classification to securing training pipelines and ensuring compliance with ISO 42001 and NIST AI RMF.
👉 Let’s secure what matters most—your data, your trust, and your AI advantage.
Book a free 30-minute consultation to assess where you stand and map out a path forward: 📅 Schedule a Call | 📩 info@deurainfosec.com
“Whether you’re a technology professional, policymaker, academic, or simply a curious reader, this book will arm you with the knowledge to navigate the complex intersection of AI, security, and society.”
In today’s fast-evolving AI landscape, rapid innovation is accompanied by serious challenges. Organizations must grapple with ethical dilemmas, data privacy issues, and uncertain regulatory environments—all while striving to stay competitive. These complexities make it critical to approach AI development and deployment with both caution and strategy.
Despite the hurdles, AI continues to unlock major advantages. From streamlining operations to improving decision-making and generating new roles across industries, the potential is undeniable. However, realizing these benefits demands responsible and transparent management of AI technologies.
That’s where ISO/IEC 42001:2023 comes into play. This global standard introduces a structured framework for implementing Artificial Intelligence Management Systems (AIMS). It empowers organizations to approach AI development with accountability, safety, and compliance at the core.
Deura InfoSec LLC (deurainfosec.com) specializes in helping businesses align with the ISO 42001 standard. Our consulting services are designed to help organizations assess AI risks, implement strong governance structures, and comply with evolving legal and ethical requirements.
We support clients in building AI systems that are not only technically sound but also trustworthy and socially responsible. Through our tailored approach, we help you realize AI’s full potential—while minimizing its risks.
If your organization is looking to adopt AI in a secure, ethical, and future-ready way, ISO Consulting LLC is your partner. Visit Deura InfoSec to discover how our ISO 42001 consulting services can guide your AI journey.
We guide company through ISO/IEC 42001 implementation, helping them design a tailored AI Management System (AIMS) aligned with both regulatory expectations and ethical standards. Our team conduct a comprehensive risk assessment, implemented governance controls, and built processes for ongoing monitoring and accountability.
👉 Visit Deura Infosec to start your AI compliance journey.
ISO 42001—the first international standard for managing artificial intelligence. Developed for organizations that design, deploy, or oversee AI, ISO 42001 is set to become the ISO 9001 of AI: a universal framework for trustworthy, transparent, and responsible AI.
“Whether you’re a technology professional, policymaker, academic, or simply a curious reader, this book will arm you with the knowledge to navigate the complex intersection of AI, security, and society.”
The ISO 42001 readiness checklist structured into ten key sections, followed by my feedback at the end:
1. Context & Scope Identify internal and external factors affecting AI use, clarify stakeholder requirements, and define the scope of your AI Management System (AIMS)
2. Leadership & Governance Secure executive sponsorship, assign AIMS responsibilities, establish an ethics‐driven AI policy, and communicate roles and accountability clearly
3. Planning Perform a gap analysis to benchmark current state, conduct a risk and opportunity assessment, set measurable AI objectives, and integrate risk practices throughout the AI lifecycle.
4. Support & Resources Dedicate resources for AIMS, create training around AI ethics, safety, and governance, raise awareness, establish communication protocols, and maintain documentation.
5. Operational Controls Outline stages of the AI lifecycle (design to monitoring), conduct risk assessments (bias, safety, legal), ensure transparency and explainability, maintain data quality and privacy, and implement incident response.
6. Change Management Implement structured change control—assessing proposed AI modifications, conducting ethical and feasibility reviews, cross‐functional governance, staged rollouts, and post‐implementation audits.
7. Performance Evaluation Monitor AIMS effectiveness using KPIs, conduct internal audits, and hold management reviews to validate performance and compliance.
8. Nonconformity & Corrective Action Identify and document nonconformities, implement corrective measures, review their efficacy, and update the AIMS accordingly.
9. Certification Preparation Collect evidence for internal audits, address gaps, assemble required documentation (including SoA), choose an accredited certification body, and finalize pre‐audit preparations .
Comprehensive but heavy: The checklist covers every facet of AI governance—from initial scoping and leadership engagement to external audits and continuous improvement.
Aligns well with ISO 27001: Many controls are familiar to ISMS practitioners, making ISO 42001 a viable extension.
Resource-intensive: Expect demands on personnel, training, documentation, and executive involvement.
Change management focus is smart: The dedication to handling AI updates (design, rollout, monitoring) is a notable strength.
Documentation is key: Templates like Statement of Applicability and impact assessment forms (e.g., AISIA) significantly streamline preparation.
Recommendation: Prioritize gap analysis early, leverage existing ISMS frameworks, and allocate clear roles—this positions you well for a smooth transition to certification readiness.
Overall, ISO 42001 readiness is achievable by taking a methodical, risk-based, and well-resourced approach. Let me know if you’d like templates or help mapping this to your current ISMS.
“Whether you’re a technology professional, policymaker, academic, or simply a curious reader, this book will arm you with the knowledge to navigate the complex intersection of AI, security, and society.”
