Security lead
Accountable for information and cyber security.
- Often held by
- a chief information security officer or head of cyber security
- Accountable for
- 6 controls
- Contributes to
- 22 controls
- Relies on
- Delivery lead (5), AI governance lead (2), Business owner (2), Data owner (2), Risk lead (1)
- Supports
- Delivery lead (10), AI governance lead (5), Risk lead (3), People lead (2), Executive sponsor (1), Data owner (1)
Accountable for
This role answers for each of these controls reaching the level the organisation has set, and holds the evidence.
AI security baseline
The organisation has adopted a recognised AI security baseline, built it into its security standards, and applies it to every use of AI.
With: AI governance lead, Delivery lead.
Evidence an assessor would expect
- The adopted baseline and the security standard or policy that references it
- Conformance records for a sample of uses of AI
- A register of baseline gaps with owners and risk acceptance decisions
- Security monitoring coverage for live uses of AI, with AI-relevant log sources
AI threat modelling
Each use of AI has its AI-specific threats assessed before it goes live and again when it changes, and the results shape its design and controls.
With: Delivery lead, Business owner.
Evidence an assessor would expect
- Agent threat models covering hidden instructions and tool misuse
- The threat modelling method or guidance, showing AI-specific threats
- Completed threat models for a sample of uses, with findings and actions
- Synthetic media impersonation covered in threat models
Security of the AI supply chain
The security of models, data, code and services obtained from others is assessed before use and managed for as long as they are in use.
With: Delivery lead, Risk lead.
Evidence an assessor would expect
- Responsibility split for each AI service obtained from others, with checks of the organisation's share and review dates
- The supply chain security assessment process and a sample of completed assessments
- A record of external AI components in use, with source and version
- Security clauses or schedules in AI supplier contracts
Preventing information leakage through AI
Sensitive information is protected from exposure through AI tools, prompts, training and outputs, with rules staff understand and technical measures that enforce them.
With: Data owner, AI governance lead.
Evidence an assessor would expect
- Rules or guidance linking information classification to AI tools
- Configuration or monitoring records for leakage prevention on approved tools
- Protection level records for models trained or tuned on protected information
AI in security incident response
Security incident management covers incidents involving AI, with playbooks, people who have the skills to handle them, and the ability to contain an affected AI service quickly.
With: Delivery lead, Business owner.
Evidence an assessor would expect
- Incident procedures and playbooks covering AI scenarios
- Records of exercises or real incidents involving AI, with lessons and actions
- Recovery playbooks, the published vulnerability disclosure route and exercise records
Security testing of AI before release
Before release, each AI system is security tested against the threats in its threat model by suitably skilled testers, including checks that it does not disclose personal or sensitive information.
With: Delivery lead, Data owner.
Evidence an assessor would expect
- Test records of attempts to make agents act outside their permissions
- Security test plans and results for a sample of releases, traced to the threat model
- Tester skills and independence records for higher-risk uses
- Safeguard bypass test results for generative AI
- Information disclosure test results
Contributes to
Another role is accountable; this one does part of the work or supplies what it needs.
Horizon scanning and external expectations
The organisation tracks changes in AI technology, regulation, peer practice and public expectation, and feeds what it finds into its AI strategy.
Accountable: AI governance lead.
AI policy
The organisation has an approved AI policy that states which uses of AI are permitted, which need approval and which are not allowed, and keeps it current.
Accountable: Executive sponsor.
Oversight forum
A cross-functional forum oversees AI use, risk and performance, with terms of reference, the right membership and a clear link to the organisation's main governance.
Accountable: AI governance lead.
AI inventory
The organisation keeps a current inventory of the AI it uses, including AI built into bought products and services, with enough detail to govern it.
Accountable: AI governance lead.
Integration with existing governance
AI governance works through the organisation's existing risk, information, data protection, security and procurement governance rather than alongside it.
Accountable: AI governance lead.
Policy compliance and exceptions
Compliance with the AI policy is monitored, and exceptions are requested, approved, recorded and reviewed.
Accountable: AI governance lead.
AI risk assessment method
The organisation uses one consistent, documented method to assess the risks of each use of AI, and the results feed its corporate risk management.
Accountable: Risk lead.
Legal and regulatory obligations
The organisation keeps a current view of the laws, regulations and standards that apply to its use of AI, with an owner for each obligation and a way of checking it is met.
Accountable: Risk lead.
Risk of AI from suppliers
Before AI in a bought product or service is used, and when it changes, the organisation assesses what it does, how it uses data, how the supplier changes the model, and what assurance the supplier offers.
Accountable: Risk lead.
Safe experimentation
Staff can try AI quickly within defined guardrails covering approved environments, data limits, time limits and exit criteria.
Accountable: Delivery lead.
Build, buy or configure approach
A consistent, documented method decides for each use of AI whether to configure tools already owned, procure, build, or wait, and the reasons are recorded.
Accountable: Delivery lead.
AI-aware procurement and contracts
Contracts for AI products and services set clear terms on data rights, intellectual property, notice of model changes, transparency and exit.
Accountable: Delivery lead.
Service management for live AI
Every live AI service has defined support, change and problem management arrangements, including handling of changes to models and prompts.
Accountable: Delivery lead.
Continuity when AI fails
Services that depend on AI can continue safely when the AI is unavailable, degraded or producing wrong results, through planned fallbacks that have been tested.
Accountable: Delivery lead.
Controlled access and sharing
AI systems and their users can reach only the data they need for the agreed purpose, and data is shared into and out of AI systems only under agreed rules.
Accountable: Data owner.
Approved AI tools and platforms
The organisation keeps a current list of approved AI tools and platforms, each with clear conditions of use, and staff know where to find it.
Accountable: Delivery lead.
Reference architecture for AI
Agreed architecture patterns set out how AI components fit into the technology estate, and new uses of AI follow them or record why not.
Accountable: Delivery lead.
Hosting and data location
Requirements for where AI is hosted and where its data is processed and stored are defined, and each use of AI is checked against them before and during live use.
Accountable: Delivery lead.
Managing models in live use
Models, prompts and their configuration are versioned and released under control, so the organisation knows what is running and can roll back to a known good version.
Accountable: Delivery lead.
Unapproved AI tools
Use of unapproved AI tools is detected, and people using them are steered to an approved alternative or a route to get their need met.
Accountable: Delivery lead.
AI literacy for all staff
Everyone in the organisation has a baseline understanding of what AI is, what it can and cannot do, and how to use it safely under the organisation's policy.
Accountable: People lead.
Role-based learning
People can get learning matched to what their role needs to do with AI, beyond the baseline for all staff, and use it in their work.
Accountable: People lead.