LLM AI Security and Governance Checklist for UK Leaders

Shipping a chatbot demo is easy. Owning the risk is not. The OWASP GenAI Security Project’s LLM Applications Cybersecurity and Governance Checklist (v1.1) exists for executives, security, privacy, legal, compliance and engineering leads who need a structured to-do list before GenAI features touch real data, real customers or real tools. Tick-box culture helps nobody; named owners and evidence do.

This page is that operational checklist angle. It is deliberately not another broad “LLM security guide.” For ranked technical risks, use the OWASP Top 10 for LLM Applications. For adversary tactic language, use MITRE ATLAS. Keeping these jobs separate makes each page more useful.

Key points

• Based on OWASP’s LLM AI cybersecurity and governance checklist themes (v1.1), adapted for UK readers.
• Cover adversarial risk, AI asset inventory, training, governance, legal review, and secure implementation/assurance.
• A checklist does not certify safety. It organises ownership and evidence.
• Independent assessment can validate weaknesses in deployed LLM features; remediation stays with you.

Context: governance before launch

UK organisations adopt GenAI through sanctioned products, embedded features and shadow IT. Each path creates data-handling, IP, privacy and security questions. OWASP’s checklist is aimed at leaders who must move quickly without gambling the firm on an unowned model call to a public API.

Attack Vector is UK-based. We identify weaknesses in scoped LLM assessments. We do not run your AI governance committee or remediate systems as part of a test fee. Lead credentials include CSTL and ChCSP.

Checklist: adversarial risk

• [ ] Threat-model how attackers could use GenAI against staff, customers and brand (voice cloning, faster phishing, deepfake support abuse).
• [ ] Review competitor and market pressure so security asks are framed as enabling safe adoption, not only saying no.
• [ ] Extend incident response playbooks for LLM-specific events: prompt abuse, data leakage via chat, runaway tool actions, cost amplification.
• [ ] Decide who declares an AI-related incident and who talks to customers or regulators.

Checklist: AI asset inventory

• [ ] Catalogue AI/LLM services in use (official and known shadow tools), with owners.
• [ ] Record model providers, versions, fine-tunes, plugins, agents and retrieval data sources.
• [ ] Note data sensitivity for each source (personal data, secrets, regulated content).
• [ ] Add AI components to SBOM / service inventory practices where they fit.
• [ ] Flag which deployments need penetration testing or AI red teaming before wider release.

Checklist: training and awareness

• [ ] Teach acceptable use: what must never be pasted into external models (credentials, unpublished personal data, privileged legal advice).
• [ ] Cover ethics, licensing and copyright basics for staff who generate content with GenAI.
• [ ] Update phishing awareness for GenAI-enhanced social engineering.
• [ ] Give developers a short brief on prompt injection and tool permission hazards before they wire agents to production systems.

Checklist: governance

• [ ] Publish an AI RACI: who is responsible, accountable, consulted and informed for AI risk.
• [ ] Approve an acceptable-use matrix for GenAI tools by role and data class.
• [ ] Enforce data classification rules technically where possible (DLP, tenant controls, approved workspaces).
• [ ] Require change control for new model endpoints, tools and retrieval indexes.
• [ ] Align AI risk reporting to existing risk committees rather than inventing a parallel universe nobody attends.

Checklist: legal, privacy and commercial

• [ ] Review vendor terms for training on your prompts, retention, subprocessors and indemnity.
• [ ] Assess IP and copyright exposure for generated outputs you will publish or ship.
• [ ] Confirm insurance language still makes sense when AI features process personal data or drive automated actions.
• [ ] Check sector duties (for example health, finance, employment monitoring) before enabling high-risk use cases.
• [ ] Document DPIA / risk assessment triggers under UK GDPR where personal data is in scope. This is process advice, not legal advice.

Checklist: implementing and assuring LLM solutions

• [ ] Threat-model trust boundaries: user input, retrieved content, tool outputs, memory stores, system prompts.
• [ ] Apply least privilege to every tool and service account the model can invoke.
• [ ] Validate and encode outputs before they hit browsers, shells, SQL or ticket systems.
• [ ] Bound consumption (rate, cost, recursion) to limit Denial-of-Wallet and runaway agents.
• [ ] Test before release: application security testing, code review where relevant, vulnerability assessment, and targeted LLM abuse cases.
• [ ] Require third-party assurance for material external AI providers when risk warrants it.
• [ ] Rehearse an LLM incident in a tabletop before production traffic peaks.

When you need independent technical validation of a deployed feature, see LLM security assessment. Engagements start from £1,500 for tightly defined scopes.

Ticking boxes without testing

The failure mode is a completed PDF with no owners, no inventory and no test evidence. The second is locking down the official chatbot while staff paste crown-jewel data into personal accounts. Governance only works if sanctioned paths are usable enough that people stop improvising.

What a completed checklist does not mean

• This page summarises OWASP checklist themes for practical use. Always read the official OWASP GenAI checklist PDF for full control wording.
• Completing a checklist does not equal regulatory approval or “AI Act compliance.”
• Attack Vector identifies weaknesses; your teams remediate.
• No assessment guarantees absence of prompt injection or data leakage.

Need an assessment of an LLM-enabled application? Email [email protected] or use the instant quote calculator. Pair this checklist with the OWASP Top 10 for LLM and MITRE ATLAS articles when you brief technical teams.

FAQ

Is this the official OWASP document?

No. It is an Attack Vector practical summary aligned to OWASP GenAI checklist themes. Use the official GenAI project checklist as the primary source.

Who should own the checklist?

Usually a named AI or security risk owner with legal, privacy, IT and engineering in the RACI. Unowned checklists do not get completed.

When do we need penetration testing?

When an LLM feature can reach sensitive data, privileged tools, multi-tenant retrieval, or customer trust boundaries, or when a buyer asks for independent assurance.

How is this different from the OWASP LLM Top 10?

The Top 10 ranks technical risks. This checklist organises governance and delivery actions for leaders and programme owners.

Does UK GDPR ban ChatGPT at work?

Not as a blanket rule. Processing personal data still needs a lawful basis, transparency and appropriate security. Policy and DPIA practice matter; get legal advice for your use case.

Will Attack Vector write our AI policy?

Our core offering here is identifying weaknesses through scoped assessment and clear reporting. Policy drafting may be discussed separately via [email protected]; do not assume it is included in a test fee.

Suggested Resources

• LLM security assessment
• OWASP Top 10 for LLM applications 2026
• MITRE ATLAS explained
• Instant quote calculator
• Penetration testing services