ISO/IEC 27001:2022 - Information Security Management

Summary

While not designed specifically for AI, ISO/IEC 27001 has become the de facto security foundation that most AI governance frameworks assume you already have in place. This international standard provides the blueprint for establishing, implementing, maintaining, and improving an Information Security Management System (ISMS) - essentially your organization's comprehensive approach to keeping sensitive data secure. For AI systems, which often process vast amounts of personal or proprietary data, ISO 27001's risk-based methodology and security controls serve as critical building blocks that more specialized AI governance standards build upon.

The Security Foundation AI Governance Assumes You Have

Most AI-specific frameworks and regulations don't reinvent information security - they assume you're already following ISO 27001 or equivalent practices. The EU AI Act references established cybersecurity standards, NIST's AI Risk Management Framework builds on existing security controls, and enterprise AI policies typically require ISO 27001 compliance as a prerequisite. This means that organizations serious about AI governance often find themselves implementing ISO 27001 first, then layering AI-specific requirements on top.

The 2022 revision strengthened requirements around cloud security, supply chain risk management, and data protection - all critical considerations for modern AI systems that rely heavily on cloud infrastructure and third-party services.

What's Actually Inside the Standard

ISO 27001 centers around 93 security controls organized into four themes: organizational (37 controls), people (8 controls), physical (14 controls), and technological (34 controls). For AI systems, particularly relevant controls include:

Data security lifecycle management

• covering data collection, processing, storage, and deletion Access control and authentication
• crucial for AI model access and training data protection
  Supplier relationship security
• essential given AI's reliance on third-party tools and cloud services Incident management
• including security breach response procedures Business continuity
• ensuring AI systems remain operational during disruptions

The standard requires a formal risk assessment process, documented security policies, regular audits, and continuous improvement - creating the systematic approach to security that AI governance frameworks assume is already in place.

Who This Resource Is For

• Essential for: CISOs and security teams at organizations deploying AI systems, compliance managers preparing for AI regulation compliance, and IT leaders building the security infrastructure needed before implementing AI governance frameworks.
• Valuable for: AI product managers who need to understand security requirements their systems must meet, legal and risk teams evaluating AI governance strategies, and consultants helping organizations prepare for AI compliance requirements.
• Consider if: You're already following a comprehensive information security framework (though ISO 27001 certification may still be required for certain AI applications), you're only doing basic AI experimentation without sensitive data, or your organization is too small to justify the formal ISMS overhead.

Getting Certified vs. Just Following the Standard

You can implement ISO 27001 practices without formal certification, but certification provides third-party validation that's increasingly required for AI applications in regulated industries. The certification process typically takes 6-12 months and involves:

1. Gap analysis - identifying current security practices vs. ISO 27001 requirements
2. ISMS implementation - documenting policies, procedures, and controls
3. Internal audit - testing your system before external assessment
4. Stage 1 audit - external auditor reviews documentation
5. Stage 2 audit - external auditor tests implementation and grants certification

Certification costs vary widely ($15K-$100K+ depending on organization size and complexity) but may be necessary for AI systems in healthcare, financial services, or government applications where security certification is mandated.

Common Implementation Pitfalls for AI Organizations

• Treating it as a checkbox exercise: ISO 27001 requires genuine risk-based thinking, not just documenting policies. AI systems introduce novel risks that require careful assessment, not just copying template policies.
• Underestimating the documentation burden: The standard requires extensive documentation of processes, decisions, and controls. AI teams used to rapid experimentation often struggle with the formal documentation requirements.
• Ignoring third-party AI services: Many organizations focus on their own systems while overlooking security requirements for AI APIs, cloud ML services, and model hosting providers they rely on.
• Inadequate scope definition: Trying to certify too broad a scope initially, or defining scope so narrowly that it excludes critical AI system components and data flows.