Jan 04 2026

AI Governance That Actually Works: Beyond Policies and Promises

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

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

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

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

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

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

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

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

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

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

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

Opinion

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

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

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

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


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

Dec 15 2025

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

Category: AI,AI Governance,AI Guardrails,ISO 42001 — disc7 @ 11:16 am

— What ISO 42001 Is and Its Purpose
ISO 42001 is a new international standard for AI governance and management systems designed to help organizations systematically manage AI-related risks and regulatory requirements. Rather than acting as a simple checklist, it sets up an ongoing framework for defining obligations, understanding how AI systems are used, and establishing controls that fit an organization’s specific risk profile. This structure resembles other ISO management system standards (such as ISO 27001) but focuses on AI’s unique challenges.

— ISO 42001’s Role in Structured Governance
At its core, ISO 42001 helps organizations build consistent AI governance practices. It encourages comprehensive documentation, clear roles and responsibilities, and formalized oversight—essentials for accountable AI development and deployment. This structured approach aligns with the EU AI Act’s broader principles, which emphasize accountability, transparency, and risk-based management of AI systems.

— Documentation and Risk Management Synergies
Both ISO 42001 and the EU AI Act call for thorough risk assessments, lifecycle documentation, and ongoing monitoring of AI systems. Implementing ISO 42001 can make it easier to maintain records of design choices, testing results, performance evaluations, and risk controls, which supports regulatory reviews and audits. This not only creates a stronger compliance posture but also prepares organizations to respond with evidence if regulators request proof of due diligence.

— Complementary Ethical and Operational Practices
ISO 42001 embeds ethical principles—such as fairness, non-discrimination, and human oversight—into the organizational governance culture. These values closely match the normative goals of the EU AI Act, which seeks to prevent harm and bias from AI systems. By internalizing these principles at the management level, organizations can more coherently translate ethical obligations into operational policies and practices that regulators expect.

— Not a Legal Substitute for Compliance Obligations
Importantly, ISO 42001 is not a legal guarantee of EU AI Act compliance on its own. The standard remains voluntary and, as of now, is not formally harmonized under the AI Act, meaning certification does not automatically confer “presumption of conformity.” The Act includes highly specific requirements—such as risk class registration, mandated reporting timelines, and prohibitions on certain AI uses—that ISO 42001’s management-system focus does not directly satisfy. ISO 42001 provides the infrastructure for strong governance, but organizations must still execute legal compliance activities in parallel to meet the letter of the law.

— Practical Benefits Beyond Compliance
Even though it isn’t a standalone compliance passport, adopting ISO 42001 offers many practical benefits. It can streamline internal AI governance, improve audit readiness, support integration with other ISO standards (like security and quality), and enhance stakeholder confidence in AI practices. Organizations that embed ISO 42001 can reduce risk of missteps, build stronger evidence trails, and align cross-functional teams for both ethical practice and regulatory readiness.

My Opinion
ISO 42001 is a valuable foundation for AI governance and a strong enabler of EU AI Act compliance—but it should be treated as the starting point, not the finish line. It helps organizations build structured processes, risk awareness, and ethical controls that align with regulatory expectations. However, because the EU AI Act’s requirements are detailed and legally enforceable, organizations must still map ISO-level controls to specific Act obligations, maintain live evidence, and fulfill procedural legal demands beyond what ISO 42001 specifies. In practice, using ISO 42001 as a governance backbone plus tailored compliance activities is the most pragmatic and defensible approach.

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Tags: AI Governance, ISO 42001

Dec 04 2025

What ISO 42001 Looks Like in Practice: Insights From Early Certifications

Category: AI,AI Governance,AI Guardrails,ISO 42001,vCISO — disc7 @ 8:59 am

What is ISO/IEC 42001:2023

  • ISO 42001 (published December 2023) is the first international standard dedicated to how organizations should govern and manage AI systems — whether they build AI, use it, or deploy it in services.
  • It lays out what the authors call an Artificial Intelligence Management System (AIMS) — a structured governance and management framework that helps companies reduce AI-related risks, build trust, and ensure responsible AI use.

Who can use it — and is it mandatory

  • Any organization — profit or non-profit, large or small, in any industry — that develops or uses AI can implement ISO 42001.
  • For now, ISO 42001 is not legally required. No country currently mandates it.
  • But adopting it proactively can make future compliance with emerging AI laws and regulations easier.

What ISO 42001 requires / how it works

  • The standard uses a “high-level structure” similar to other well-known frameworks (like ISO 27001), covering organizational context, leadership, planning, support, operations, performance evaluation, and continual improvement.
  • Organizations need to: define their AI-policy and scope; identify stakeholders and expectations; perform risk and impact assessments (on company level, user level, and societal level); implement controls to mitigate risks; maintain documentation and records; monitor, audit, and review the AI system regularly; and continuously improve.
  • As part of these requirements, there are 38 example controls (in the standard’s Annex A) that organizations can use to reduce various AI-related risks.

Why it matters

  • Because AI is powerful but also risky (wrong outputs, bias, privacy leaks, system failures, etc.), having a formal governance framework helps companies be more responsible and transparent when deploying AI.
  • For organizations that want to build trust with customers, regulators, or partners — or anticipate future AI-related regulations — ISO 42001 can serve as a credible, standardized foundation for AI governance.

My opinion

I think ISO 42001 is a valuable and timely step toward bringing some order and accountability into the rapidly evolving world of AI. Because AI is so flexible and can be used in many different contexts — some of them high-stakes — having a standard framework helps organizations think proactively about risk, ethics, transparency, and responsibility rather than scrambling reactively.

That said — because it’s new and not yet mandatory — its real-world impact depends heavily on how widely it’s adopted. For it to become meaningful beyond “nice to have,” regulators, governments, or large enterprises should encourage or require it (or similar frameworks). Until then, it will likely be adopted mostly by forward-thinking companies or those dealing with high-impact AI systems.

🔎 My view: ISO 42001 is a meaningful first step — but (for now) best seen as a foundation, not a silver bullet

I believe ISO 42001 represents a valuable starting point for bringing structure, accountability, and risk awareness to AI development and deployment. Its emphasis on governance, impact assessment, documentation, and continuous oversight is much needed in a world where AI adoption often runs faster than regulation or best practices.

That said — given its newness, generality, and the typical resource demands — I see it as necessary but not sufficient. It should be viewed as the base layer: useful for building internal discipline, preparing for regulatory demands, and signaling commitment. But to address real-world ethical, social, and technical challenges, organizations likely need additional safeguards — e.g. context-specific controls, ongoing audits, stakeholder engagement, domain-specific reviews, and perhaps even bespoke governance frameworks tailored to the type of AI system and its use cases.

In short: ISO 42001 is a strong first step — but real responsible AI requires going beyond standards to culture, context, and continuous vigilance.

✅ Real-world adopters of ISO 42001

IBM (Granite models)

  • IBM became “the first major open-source AI model developer to earn ISO 42001 certification,” for its “Granite” family of open-source language models.
  • The certification covers the management system for development, deployment, and maintenance of Granite — meaning IBM formalized policies, governance, data practices, documentation, and risk controls under AIMS (AI Management System).
  • According to IBM, the certification provides external assurance of transparency, security, and governance — helping enterprises confidently adopt Granite in sensitive contexts (e.g. regulated industries).

Infosys

  • Infosys — a global IT services and consulting company — announced in May 2024 that it had received ISO 42001:2023 certification for its AI Management System.
  • Their certified “AIMS framework” is part of a broader set of offerings (the “Topaz Responsible AI Suite”), which supports clients in building and deploying AI responsibly, with structured risk mitigations and accountability.
  • This demonstrates that even big consulting companies, not just pure-AI labs, see value in adopting ISO 42001 to manage AI at scale within enterprise services.

JAGGAER (Source-to-Pay / procurement software)

  • JAGGAER — a global player in procurement / “source-to-pay” software — announced that it achieved ISO 42001 certification for its AI Management System in June 2025.
  • For JAGGAER, the certification reflects a commitment to ethical, transparent, secure deployment of AI within its procurement platform.
  • This shows how ISO 42001 can be used not only by AI labs or consultancy firms, but by business-software companies integrating AI into domain-specific applications.

🧠 My take — promising first signals, but still early days

These early adopters make a strong case that ISO 42001 can work in practice across very different kinds of organizations — not just AI-native labs, but enterprises, service providers, even consulting firms. The variety and speed of adoption (multiple firms in 2024–2025) demonstrate real momentum.

At the same time — adoption appears selective, and for many companies, the process may involve minimal compliance effort rather than deep, ongoing governance. Because the standard and the ecosystem (auditors, best-practice references, peer case studies) are both still nascent, there’s a real risk that ISO 42001 becomes more of a “badge” than a strong guardrail.

In short: I see current adoptions as proof-of-concepts — promising early examples showing how ISO 42001 could become an industry baseline. But for it to truly deliver on safe, ethical, responsible AI at scale, we’ll need: more widespread adoption across sectors; shared transparency about governance practices; public reporting on outcomes; and maybe supplementary audits or domain-specific guidelines (especially for high-risk AI uses).

Most organizations think they’re ready for AI governance — until ISO/IEC 42001 shines a light on the gaps. With 47 new AI-specific controls, this standard is quickly becoming the global expectation for responsible and compliant AI deployment. To help teams get ahead, we built a free ISO 42001 Compliance Checklist that gives you a readiness score in under 10 minutes, plus a downloadable gap report you can share internally. It’s a fast way to validate where you stand today and what you’ll need to align with upcoming regulatory and customer requirements. If improving AI trust, risk posture, and audit readiness is on your roadmap, this tool will save your team hours.

https://blog.deurainfosec.com/free-iso-42001-compliance-checklist-assess-your-ai-governance-readiness-in-10-minutes/

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Tags: ISO 42001

Nov 16 2025

ISO/IEC 42001: The New Blueprint for Trustworthy and Responsible AI Governance

Category: AI,AI Governance,AI Governance Tools,AI Guardrails,ISO 42001 — disc7 @ 11:19 pm

Artificial intelligence is rapidly advancing, prompting countries and industries worldwide to introduce new rules, norms, and governance frameworks. ISO/IEC 42001 represents a major milestone in this global movement by formalizing responsible AI management. It does so through an Artificial Intelligence Management System (AIMS) that guides organizations in overseeing AI systems safely and transparently throughout their lifecycle.

Achieving certification under ISO/IEC 42001 demonstrates that an organization manages its AI—from strategy and design to deployment and retirement—with accountability and continuous improvement. The standard aligns with related ISO guidelines covering terminology, impact assessment, and certification body requirements, creating a unified and reliable approach to AI governance.

The certification journey begins with defining the scope of the organization’s AI activities. This includes identifying AI systems, use cases, data flows, and related business processes—especially those that rely on external AI models or third-party services. Clarity in scope enables more effective governance and risk assessment across the AI portfolio.

A robust risk management system is central to compliance. Organizations must identify, evaluate, and mitigate risks that arise throughout the AI lifecycle. This is supported by strong data governance practices, ensuring that training, validation, and testing datasets are relevant, representative, and as accurate as possible. These foundations enable AI systems to perform reliably and ethically.

Technical documentation and record-keeping also play critical roles. Organizations must maintain detailed materials that demonstrate compliance and allow regulators or auditors to evaluate the system. They must also log lifecycle events—such as updates, model changes, and system interactions—to preserve traceability and accountability over time.

Beyond documentation, organizations must ensure that AI systems are used responsibly in the real world. This includes providing clear instructions to downstream users, maintaining meaningful human oversight, and ensuring appropriate accuracy, robustness, and cybersecurity. These operational safeguards anchor the organization’s quality management system and support consistent, repeatable compliance.

Ultimately, ISO/IEC 42001 delivers major benefits by strengthening trust, improving regulatory readiness, and embedding operational discipline into AI governance. It equips organizations with a structured, audit-ready framework that aligns with emerging global regulations and moves AI risk management into an ongoing, sustainable practice rather than a one-time effort.

My opinion:
ISO/IEC 42001 is arriving at exactly the right moment. As AI systems become embedded in critical business functions, organizations need more than ad-hoc policies—they need a disciplined management system that integrates risk, governance, and accountability. This standard provides a practical blueprint and gives vCISOs, compliance leaders, and innovators a common language to build trustworthy AI programs. Those who adopt it early will not only reduce risk but also gain a significant competitive and credibility advantage in an increasingly regulated AI ecosystem.

ISO/IEC 42001:2023 – Implementing and Managing AI Management Systems (AIMS): Practical Guide

Check out our earlier posts on AI-related topics: AI topic

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A practical, business‑first service to help your organization adopt AI confidently while staying compliant with ISO/IEC 42001, NIST AI RMF, and emerging global AI regulations.

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1. AI Risk & Readiness Assessment (Fast — 7 Days)

  • Identify all AI use cases + shadow AI
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  • AI Use Policy (employee‑friendly)
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Tags: AI Governance, AIMS, ISO 42001

Oct 08 2025

ISO 42001: The New Benchmark for Responsible AI Governance and Security

Category: AI,AI Governance,AI Guardrails,ISO 42001 — disc7 @ 10:42 am

AI governance and security have become central priorities for organizations expanding their use of artificial intelligence. As AI capabilities evolve rapidly, businesses are seeking structured frameworks to ensure their systems are ethical, compliant, and secure. ISO 42001 certification has emerged as a key tool to help address these growing concerns, offering a standardized approach to managing AI responsibly.

Across industries, global leaders are adopting ISO 42001 as the foundation for their AI governance and compliance programs. Many leading technology companies have already achieved certification for their core AI services, while others are actively preparing for it. For AI builders and deployers alike, ISO 42001 represents more than just compliance — it’s a roadmap for trustworthy and transparent AI operations.

The certification process provides a structured way to align internal AI practices with customer expectations and regulatory requirements. It reassures clients and stakeholders that AI systems are developed, deployed, and managed under a disciplined governance framework. ISO 42001 also creates a scalable foundation for organizations to introduce new AI services while maintaining control and accountability.

For companies with established Governance, Risk, and Compliance (GRC) functions, ISO 42001 certification is a logical next step. Pursuing it signals maturity, transparency, and readiness in AI governance. The process encourages organizations to evaluate their existing controls, uncover gaps, and implement targeted improvements — actions that are critical as AI innovation continues to outpace regulation.

Without external validation, even innovative companies risk falling behind. As AI technology evolves and regulatory pressure increases, those lacking a formal governance framework may struggle to prove their trustworthiness or readiness for compliance. Certification, therefore, is not just about checking a box — it’s about demonstrating leadership in responsible AI.

Achieving ISO 42001 requires strong executive backing and a genuine commitment to ethical AI. Leadership must foster a culture of responsibility, emphasizing secure development, data governance, and risk management. Continuous improvement lies at the heart of the standard, demanding that organizations adapt their controls and oversight as AI systems grow more complex and pervasive.

In my opinion, ISO 42001 is poised to become the cornerstone of AI assurance in the coming decade. Just as ISO 27001 became synonymous with information security credibility, ISO 42001 will define what responsible AI governance looks like. Forward-thinking organizations that adopt it early will not only strengthen compliance and customer trust but also gain a strategic advantage in shaping the ethical AI landscape.

ISO/IEC 42001: Catalyst or Constraint? Navigating AI Innovation Through Responsible Governance


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Oct 07 2025

ISO/IEC 42001: Catalyst or Constraint? Navigating AI Innovation Through Responsible Governance

Category: AI,AI Governance,AI Guardrails,ISO 42001 — disc7 @ 11:48 am

🌐 “Does ISO/IEC 42001 Risk Slowing Down AI Innovation, or Is It the Foundation for Responsible Operations?”

🔍 Overview

The post explores whether ISO/IEC 42001—a new standard for Artificial Intelligence Management Systems—acts as a barrier to AI innovation or serves as a framework for responsible and sustainable AI deployment.

🚀 AI Opportunities

ISO/IEC 42001 is positioned as a catalyst for AI growth:

  • It helps organizations understand their internal and external environments to seize AI opportunities.
  • It establishes governance, strategy, and structures that enable responsible AI adoption.
  • It prepares organizations to capitalize on future AI advancements.

🧭 AI Adoption Roadmap

A phased roadmap is suggested for strategic AI integration:

  • Starts with understanding customer needs through marketing analytics tools (e.g., Hootsuite, Mixpanel).
  • Progresses to advanced data analysis and optimization platforms (e.g., GUROBI, IBM CPLEX, Power BI).
  • Encourages long-term planning despite the fast-evolving AI landscape.

🛡️ AI Strategic Adoption

Organizations can adopt AI through various strategies:

  • Defensive: Mitigate external AI risks and match competitors.
  • Adaptive: Modify operations to handle AI-related risks.
  • Offensive: Develop proprietary AI solutions to gain a competitive edge.

⚠️ AI Risks and Incidents

ISO/IEC 42001 helps manage risks such as:

  • Faulty decisions and operational breakdowns.
  • Legal and ethical violations.
  • Data privacy breaches and security compromises.

🔐 Security Threats Unique to AI

The presentation highlights specific AI vulnerabilities:

  • Data Poisoning: Malicious data corrupts training sets.
  • Model Stealing: Unauthorized replication of AI models.
  • Model Inversion: Inferring sensitive training data from model outputs.

🧩 ISO 42001 as a GRC Framework

The standard supports Governance, Risk Management, and Compliance (GRC) by:

  • Increasing organizational resilience.
  • Identifying and evaluating AI risks.
  • Guiding appropriate responses to those risks.

🔗 ISO 27001 vs ISO 42001

  • ISO 27001: Focuses on information security and privacy.
  • ISO 42001: Focuses on responsible AI development, monitoring, and deployment.

Together, they offer a comprehensive risk management and compliance structure for organizations using or impacted by AI.

🏗️ Implementing ISO 42001

The standard follows a structured management system:

  • Context: Understand stakeholders and external/internal factors.
  • Leadership: Define scope, policy, and internal roles.
  • Planning: Assess AI system impacts and risks.
  • Support: Allocate resources and inform stakeholders.
  • Operations: Ensure responsible use and manage third-party risks.
  • Evaluation: Monitor performance and conduct audits.
  • Improvement: Drive continual improvement and corrective actions.

💬 My Take

ISO/IEC 42001 doesn’t hinder innovation—it channels it responsibly. In a world where AI can both empower and endanger, this standard offers a much-needed compass. It balances agility with accountability, helping organizations innovate without losing sight of ethics, safety, and trust. Far from being a brake, it’s the steering wheel for AI’s journey forward.

Would you like help applying ISO 42001 principles to your own organization or project?

Feel free to contact us if you need assistance with your AI management system.

ISO/IEC 42001 can act as a catalyst for AI innovation by providing a clear framework for responsible governance, helping organizations balance creativity with compliance. However, if applied rigidly without alignment to business goals, it could become a constraint that slows decision-making and experimentation.

AIMS and Data Governance – Managing data responsibly isn’t just good practice—it’s a legal and ethical imperative. 

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Tags: AI Governance, ISO 42001

Oct 01 2025

10 Steps needed to build AIMS ISO 42001

Category: AI,ISO 42001 — disc7 @ 10:10 am

Key steps to build an AI Management System (AIMS) compliant with ISO 42001:

Steps to Build an AIMS (ISO 42001)

1. Establish Context & Scope

  • Define your organization’s AI activities and objectives
  • Identify internal and external stakeholders
  • Determine the scope and boundaries of your AIMS
  • Understand applicable legal and regulatory requirements

2. Leadership & Governance

  • Secure top management commitment and resources
  • Establish AI governance structure and assign roles/responsibilities
  • Define AI policies aligned with organizational values
  • Appoint an AI management representative

3. Risk Assessment & Planning

  • Identify AI-related risks and opportunities
  • Conduct impact assessments (bias, privacy, safety, security)
  • Define risk acceptance criteria
  • Create risk treatment plans with controls

4. Develop AI Policies & Procedures

  • Create AI usage policies and ethical guidelines
  • Document AI lifecycle processes (design, development, deployment, monitoring)
  • Establish data governance and quality requirements
  • Define incident response and escalation procedures

5. Resource Management

  • Allocate necessary resources (people, technology, budget)
  • Ensure competence through training and awareness programs
  • Establish infrastructure for AI operations
  • Create documentation and knowledge management systems

6. AI System Development Controls

  • Implement secure development practices
  • Establish model validation and testing procedures
  • Create explainability and transparency mechanisms
  • Define human oversight requirements

7. Operational Controls

  • Deploy monitoring and performance tracking
  • Implement change management processes
  • Establish data quality and integrity controls
  • Create audit trails and logging systems

8. Performance Monitoring

  • Define and track key performance indicators (KPIs)
  • Monitor AI system outputs for drift, bias, and errors
  • Conduct regular internal audits
  • Review effectiveness of controls

9. Continuous Improvement

  • Address non-conformities and take corrective actions
  • Capture lessons learned and best practices
  • Update policies based on emerging risks and regulations
  • Conduct management reviews periodically

10. Certification Preparation

  • Conduct gap analysis against ISO 42001 requirements
  • Engage with certification bodies
  • Perform pre-assessment audits
  • Prepare documentation for formal certification audit

Key Documentation Needed:

  • AI Policy & Objectives
  • Risk Register & Treatment Plans
  • Procedures & Work Instructions
  • Records of Decisions & Approvals
  • Training Records
  • Audit Reports
  • Incident Logs

Contact us if you’d like me to share a detailed implementation checklist or project plan for these steps.

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Tags: AIMS, ISO 42001

Sep 26 2025

Aligning risk management policy with ISO 42001 requirements

Category: AI,Information Security,ISO 42001,Security Risk Assessment — disc7 @ 11:11 am

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.

AI_Risk_Management_Policy_ISO42001Download

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Sep 22 2025

ISO 42001:2023 Control Gap Assessment – Your Roadmap to Responsible AI Governance

Category: AI,AI Governance,AI Governance Tools,ISO 42001 — disc7 @ 8:35 am

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 our ISO 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 with ISO 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.

Act now! For a limited time only, we’re offering a FREE assessment of any one of the nine control objectives. Don’t miss this chance to gain expert insights at no cost—claim your free assessment today before the offer expires!

Let us help you strengthen AI Governance with a thorough ISO 42001 controls assessment — contact us now… info@deurainfosec.com

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.

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Sep 18 2025

Managing AI Risk: Building a Risk-Aware Strategy with ISO 42001, ISO 27001, and NIST

Category: AI,AI Governance,CISO,ISO 27k,ISO 42001,vCISO — disc7 @ 7:59 am

Managing AI Risk: A Practical Approach to Responsibly Managing AI with ISO 42001 treats building a risk-aware strategy, relevant standards (ISO 42001, ISO 27001, NIST, etc.), the role of an Artificial Intelligence Management System (AIMS), and what the future of AI risk management might look like.

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.

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Tags: iso 27001, ISO 42001, Managing AI Risk, NIST

Sep 11 2025

ISO/IEC 42001: The Global Standard for Responsible AI Governance, Risk, and Compliance

Category: AI,AI Governance,ISO 42001 — disc7 @ 4:22 pm

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.

Feel free to reach out if you have any questions.

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Aug 26 2025

From Compliance to Trust: Rethinking Security in 2025

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

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

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

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

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

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

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

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

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

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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.

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Aug 21 2025

ISO/IEC 42001 Requirements Mapped to ShareVault

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

🏢 Strategic Benefits for ShareVault

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

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

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

🗺️ ISO/IEC 42001 Implementation Roadmap for ShareVault

Phase 1: Initiation & Scoping

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

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

Phase 2: Gap Analysis & Risk Assessment

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

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

Phase 3: Policy & Governance Framework

🔹 Objective: Establish foundational policies and oversight mechanisms.

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

Phase 4: Data & Model Governance

🔹 Objective: Ensure trustworthy data and model practices.

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

Phase 5: Operational Controls & Monitoring

🔹 Objective: Embed AI governance into daily operations.

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

Phase 6: Stakeholder Engagement & Transparency

🔹 Objective: Build trust with users and clients.

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

Phase 7: Certification & Continuous Improvement

🔹 Objective: Achieve compliance and evolve responsibly.

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

🧠 Bonus Tip: Align with Other Standards

ShareVault can integrate ISO 42001 with:

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

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

🗂️ ISO 42001 Implementation Roadmap for VDR Providers

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

📌 Milestone Highlights

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

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Tags: ISO 42001, Sharevault

Aug 04 2025

ISO 42001: The AI Governance Standard Every Organization Needs to Understand

Category: AI,ISO 42001,IT Governance — disc7 @ 3:29 pm

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.

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Jul 18 2025

Mitigate and adapt with AICM (AI Controls Matrix)

Category: AI,ISO 42001 — disc7 @ 9:03 am

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 NameExample Controls Count
1Governance & Leadership15
2Risk Management14
3Compliance & Legal13
4AI Ethics & Responsible AI18
5Data Governance16
6Model Lifecycle Management17
7Privacy & Data Protection15
8Security Architecture13
9Secure Development Practices15
10Threat Detection & Response12
11Monitoring & Logging12
12Access Control14
13Supply Chain Security13
14Business Continuity & Resilience12
15Human Factors & Awareness14
16Incident Management14
17Performance & Explainability13
18Third-Party Risk Management13
+———————————————————————————+
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:

  1. Audit and Assurance
  2. Application and Interface Security
  3. Business Continuity Management and Operational Resilience
  4. Change Control and Configuration Management
  5. Cryptography, Encryption and Key Management
  6. Datacenter Security
  7. Data Security and Privacy Lifecycle Management
  8. Governance, Risk and Compliance
  9. Human Resources
  10. Identity and Access Management (IAM)
  11. Interoperability and Portability
  12. Infrastructure Security
  13. Logging and Monitoring
  14. Model Security
  15. Security Incident Management, E‑Discovery & Cloud Forensics
  16. Supply Chain Management, Transparency and Accountability
  17. Threat & Vulnerability Management
  18. Universal Endpoint Management

Gap Analysis Template based on AICM (Artificial Intelligence Control Matrix)

#DomainControl ObjectiveCurrent State (1-5)Target State (1-5)GapResponsibleEvidence/NotesRemediation ActionDue Date
1Governance & LeadershipAI governance structure is formally defined.253John D.No documented AI policyDraft governance charter2025-08-01
2Risk ManagementAI risk taxonomy is established and used.341Priya M.Partial mappingAlign with ISO 238942025-07-25
3Privacy & Data ProtectionAI models trained on PII have privacy controls.154Sarah W.Privacy review not performedConduct DPIA2025-08-10
4AI Ethics & Responsible AIAI systems are evaluated for bias and fairness.253Ethics BoardInformal process onlyImplement AI fairness tools2025-08-15
…………………………

🔢 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

Download info and 7 minute intro video: https://lnkd.in/gZmWkQ8V

#AIGuardrails #CSA #AIControlsMatrix #AICM

🎯 Use Case: ISO/IEC 42001-Based AI Governance Gap Analysis (Customized AICM)

#AICM DomainISO 42001 ClauseControl ObjectiveCurrent State (1–5)Target State (1–5)GapResponsibleEvidence/NotesRemediation ActionDue Date
1Governance & Leadership5.1 LeadershipLeadership demonstrates AI responsibility and commitment253CTONo AI charter signed by execsFormalize AI governance charter2025-08-01
2Risk Management6.1 Actions to address risksAI risk register and risk criteria are defined and maintained341Risk LeadRisk register lacks AI-specific itemsIntegrate AI risks into enterprise ERM2025-08-05
3AI Ethics & Responsible AI6.3 Ethical impact assessmentAI system ethical impact is documented and reviewed periodically154Ethics TeamNo structured ethical reviewCreate ethics impact assessment process2025-08-15
4Data Governance8.3 Data & data qualityData used in AI is validated, labeled, and assessed for bias253Data OwnerInconsistent labeling practicesImplement AI data QA framework2025-08-20
5Model Lifecycle Management8.2 AI lifecycleAI lifecycle stages are defined and documented (from design to EOL)253ML LeadNo documented lifecycleAdopt ISO 42001 lifecycle guidance2025-08-30
6Privacy & Data Protection8.3.2 Privacy & PIIPII used in AI training is minimized, protected, and compliant253DPONo formal PII minimization strategyConduct AI-focused DPIAs2025-08-10
7Monitoring & Logging9.1 MonitoringAI systems are continuously monitored for drift, bias, and failure352DevOpsLogging enabled, no alerts setAutomate AI model monitoring2025-09-01
8Performance & Explainability8.4 ExplainabilityModels provide human-understandable decisions where needed143AI TeamBlack-box model in productionAdopt SHAP/LIME/XAI tools2025-09-10
……………………………

🧭 Scoring Scale:

  • 1 – Not Implemented
  • 2 – Partially Implemented
  • 3 – Implemented but not Audited
  • 4 – Audited and Maintained
  • 5 – Integrated and Continuously Improved

🔗 Key Mapping to ISO/IEC 42001 Sections:

  • Clause 4: Context of the organization
  • Clause 5: Leadership
  • Clause 6: Planning (risk, opportunities, impact)
  • Clause 7: Support (resources, awareness, documentation)
  • Clause 8: Operation (AI lifecycle, data, privacy)
  • Clause 9: Performance evaluation (monitoring, audit)
  • Clause 10: Improvement (nonconformity, corrective action)

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Tags: #AI Guardrails, #CSA, AI Controls Matrix, AICM, Controls Matrix, EU AI Act, iso 27001, ISO 42001, NIST AI Risk Management Framework

Jul 06 2025

Turn Compliance into Competitive Advantage with ISO 42001

Category: AI,Information Security,ISO 42001 — disc7 @ 10:49 pm

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.


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Jul 02 2025

 ISO/IEC 42001:2023 – from establishing to maintain an AI management system

Category: AI — disc7 @ 12:06 pm

AI businesses are at risk due to growing cyber threats, regulatory pressure, and ethical concerns. They often process vast amounts of sensitive data, making them prime targets for breaches and data misuse. Malicious actors can exploit AI systems through model manipulation, adversarial inputs, or unauthorized access. Additionally, lack of standardized governance and compliance frameworks exposes them to legal and reputational damage. As AI adoption accelerates, so do the risks.

AI businesses are at risk because they often handle large volumes of sensitive data, rely on complex algorithms that may be vulnerable to manipulation, and operate in a rapidly evolving regulatory landscape. Threats include data breaches, model poisoning, IP theft, bias in decision-making, and misuse of AI tools by attackers. Additionally, unclear accountability and lack of standardized AI security practices increase their exposure to legal, reputational, and operational risks.

Why it matters

It matters because the integrity, security, and trustworthiness of AI systems directly impact business reputation, customer trust, and regulatory compliance. A breach or misuse of AI can lead to financial loss, legal penalties, and harm to users. As AI becomes more embedded in critical decision-making—like healthcare, finance, and security—the risks grow more severe. Ensuring responsible and secure AI isn’t just good practice—it’s essential for long-term success and societal trust.

To reduce risks in AI businesses, we can:

  1. Implement strong governance with AIMS – Define clear accountability, policies, and oversight for AI development and use.
  2. Secure data and models – Encrypt sensitive data, restrict access, and monitor for tampering or misuse.
  3. Conduct risk assessments – Regularly evaluate threats, vulnerabilities, and compliance gaps in AI systems.
  4. Ensure transparency and fairness – Use explainable AI and audit algorithms for bias or unintended consequences.
  5. Stay compliant – Align with evolving regulations like GDPR, NIST AI RMF, or the EU AI Act.
  6. Train teams – Educate employees on AI ethics, security best practices, and safe use of generative tools.

Proactive risk management builds trust, protects assets, and positions AI businesses for sustainable growth.

 ISO/IEC 42001:2023 – from establishing to maintain an AI management system (AIMS)

BSI ISO 31000 is standard for any organization seeking risk management guidance

ISO/IEC 27001 and ISO/IEC 42001, both standards address risk and management systems, but with different focuses. ISO/IEC 27001 is centered on information security—protecting data confidentiality, integrity, and availability—while ISO/IEC 42001 is the first standard designed specifically for managing artificial intelligence systems responsibly. ISO/IEC 42001 includes considerations like AI-specific risks, ethical concerns, transparency, and human oversight, which are not fully addressed in ISO 27001. Organizations working with AI should not rely solely on traditional information security controls.

While ISO/IEC 27001 remains critical for securing data, ISO/IEC 42001 complements it by addressing broader governance and accountability issues unique to AI. The article suggests that companies developing or deploying AI should integrate both standards to build trust and meet growing stakeholder and regulatory expectations. Applying ISO 42001 can help demonstrate responsible AI practices, ensure explainability, and mitigate unintended consequences, positioning organizations to lead in a more regulated AI landscape.

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Jun 19 2025

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

Category: AI,Information Security — disc7 @ 9:14 am

Mapping against ISO 42001:2023 and/or the EU Artificial Intelligence (AI) Act

The AI Act & ISO 42001 Gap Analysis Tool is a dual-purpose resource that helps organizations assess their current AI practices against both legal obligations under the EU AI Act and international standards like ISO/IEC 42001:2023. It allows users to perform a tailored gap analysis based on their specific needs, whether aligning with ISO 42001, the EU AI Act, or both. The tool facilitates early-stage project planning by identifying compliance gaps and setting actionable priorities.

With the EU AI Act now in force and enforcement of its prohibitions on high-risk AI systems beginning in February 2025, organizations face growing pressure to proactively manage AI risk. Implementing an AI management system (AIMS) aligned with ISO 42001 can reduce compliance risk and meet rising international expectations. As AI becomes more embedded in business operations, conducting a gap analysis has become essential for shaping a sound, legally compliant, and responsible AI strategy.

Feedback:
This tool addresses a timely and critical need in the AI governance landscape. By combining legal and best-practice assessments into one streamlined solution, it helps reduce complexity for compliance teams. Highlighting the upcoming enforcement deadlines and the benefits of ISO 42001 certification reinforces urgency and practicality.

The AI Act & ISO 42001 Gap Analysis Tool is a user-friendly solution that helps organizations quickly and effectively assess their current AI practices against both the EU AI Act and the ISO/IEC 42001:2023 standard. With intuitive features, customizable inputs, and step-by-step guidance, the tool adapts to your organization’s specific needs—whether you’re looking to meet regulatory obligations, align with international best practices, or both. Its streamlined interface allows even non-technical users to conduct a thorough gap analysis with minimal training.

Designed to integrate seamlessly into your project planning process, the tool delivers clear, actionable insights into compliance gaps and priority areas. As enforcement of the EU AI Act begins in early 2025, and with increasing global focus on AI governance, this tool provides not only legal clarity but also practical, accessible support for developing a robust AI management system. By simplifying the complexity of AI compliance, it empowers teams to make informed, strategic decisions faster.

What does the tool provide?

  • Split into two sections, EU AI Act and ISO 42001, so you can perform analyses for both or an individual analysis.
  • The EU AI Act section is divided into six sets of questions: general requirements, entity requirements, assessment and registration, general-purpose AI, measures to support innovation and post-market monitoring.
  • Identify which requirements and sections of the AI Act are applicable by completing the provided screening questions. The tool will automatically remove any non-applicable questions.
  • The ISO 42001 section is divided into two sets of questions: ISO 42001 six clauses and ISO 42001 controls as outlined in Annex A.
  • Executive summary pages for both analyses, including by section or clause/control, the number of requirements met and compliance percentage totals.
  • A clear indication of strong and weak areas through colour-coded analysis graphs and tables to highlight key areas of development and set project priorities.

The tool is designed to work in any Microsoft environment; it does not need to be installed like software, and does not depend on complex databases. It is reliant on human involvement.

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May 23 2025

Interpretation of Ethical AI Deployment under the EU AI Act

Category: AI — disc7 @ 5:39 am

Scenario: A healthcare startup in the EU develops an AI system to assist doctors in diagnosing skin cancer from images. The system uses machine learning to classify lesions as benign or malignant.

1. Risk-Based Classification

  • EU AI Act Requirement: Classify the AI system into one of four risk categories: unacceptable, high-risk, limited-risk, minimal-risk.
  • Interpretation in Scenario:
    The diagnostic system qualifies as a high-risk AI because it affects people’s health decisions, thus requiring strict compliance with specific obligations.

2. Data Governance & Quality

  • EU AI Act Requirement: High-risk AI systems must use high-quality datasets to avoid bias and ensure accuracy.
  • Interpretation in Scenario:
    The startup must ensure that training data are representative of all demographic groups (skin tones, age ranges, etc.) to reduce bias and avoid misdiagnosis.

3. Transparency & Human Oversight

  • EU AI Act Requirement: Users should be aware they are interacting with an AI system; meaningful human oversight is required.
  • Interpretation in Scenario:
    Doctors must be clearly informed that the diagnosis is AI-assisted and retain final decision-making authority. The system should offer explainability features (e.g., heatmaps on images to show reasoning).

4. Robustness, Accuracy, and Cybersecurity

  • EU AI Act Requirement: High-risk AI systems must be technically robust and secure.
  • Interpretation in Scenario:
    The AI tool must maintain high accuracy under diverse conditions and protect patient data from breaches. It should include fallback mechanisms if anomalies are detected.

5. Accountability and Documentation

  • EU AI Act Requirement: Maintain detailed technical documentation and logs to demonstrate compliance.
  • Interpretation in Scenario:
    The startup must document model architecture, training methodology, test results, and monitoring processes, and be ready to submit these to regulators if required.

6. Registration and CE Marking

  • EU AI Act Requirement: High-risk systems must be registered in an EU database and undergo conformity assessments.
  • Interpretation in Scenario:
    The startup must submit their system to a notified body, demonstrate compliance, and obtain CE marking before deployment.

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May 15 2025

From Oversight to Override: Enforcing AI Safety Through Infrastructure

Category: AI,Information Security — disc7 @ 9:57 am

You can’t have AI without an IA

As AI systems become increasingly integrated into critical sectors such as finance, healthcare, and defense, their unpredictable and opaque behavior introduces significant risks to society. Traditional safety protocols may not be sufficient to manage the potential threats posed by highly advanced AI, especially those capable of causing existential harm. To address this, researchers propose Guillotine, a hypervisor-based architecture designed to securely sandbox powerful AI models.

Guillotine leverages established virtualization techniques but also introduces fundamentally new isolation strategies tailored for AI with existential-risk potential. Unlike typical software, such AI may attempt to analyze and subvert the very systems meant to contain them. This requires a deep co-design of hypervisor software with the underlying hardware—CPU, memory, network interfaces, and storage—to prevent side-channel leaks and eliminate avenues for reflective exploitation.

Beyond technical isolation, Guillotine incorporates physical fail-safes inspired by systems in nuclear power plants and aviation. These include hardware-level disconnection mechanisms and even radical approaches like data center flooding to forcibly shut down or destroy rogue AI. These physical controls offer a final layer of defense should digital barriers fail.

The underlying concern is that many current AI safety frameworks rely on policy rather than technical enforcement. As AI becomes more capable, it may learn to bypass or manipulate these soft controls. Guillotine directly confronts this problem by embedding enforcement into the architecture itself—creating systems that can’t be talked out of enforcing the rules.

In essence, Guillotine represents a shift from trust-based AI safety toward hardened, tamper-resistant infrastructure. It acknowledges that if AI is to be trusted with mission-critical roles—or if it poses existential threats—we must engineer control systems with the same rigor and physical safeguards used in other high-risk industries.

 Guillotine: Hypervisors for Isolating Malicious AIs.

Google‘s AI-Powered Countermeasures Against Cyber Scams

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

The Role of AI in Modern Hacking: Both an Asset and a Risk

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

NIST: AI/ML Security Still Falls Short

DISC InfoSec’s earlier post on the AI topic

Trust Me – ISO 42001 AI Management System

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

What You Are Not Told About ChatGPT: Key Insights into the Inner Workings of ChatGPT & How to Get the Most Out of It

Digital Ethics in the Age of AI – Navigating the ethical frontier today and beyond

Artificial intelligence – Ethical, social, and security impacts for the present and the future

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Tags: AIMS, AISafety, artificial intelligence, Enforcing AI Safety, GuillotineAI, information architecture, ISO 42001

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