AI Security Awareness Training: turning curious clicks into confident, compliant decisions 

• by Olivia Lawlor-Blackburn
• Advent IM Blog

Artificial Intelligence isn’t just another tool on the belt; it’s a power tool with a turbo button. Used wisely, it speeds delivery, improves quality, and frees humans for higher-value work. Used carelessly, it can leak sensitive data, entrench bias, or quietly route your organisation’s IP into somebody else’s model. That’s why AI Security Awareness Training has moved from “nice to have” to basic operational hygiene. 

This post sets out what good AI awareness looks like, why UK organisations—especially Government, Defence and CNI—need it now, and how to make training stick. 

Why now? 

UK policy and guidance have matured. Government expectations are clear: raise awareness of AI threats, design for security, manage risk across the lifecycle, ensure accountable human oversight, and treat supply chains as part of your attack surface. The NCSC has also set out practical guidance for secure AI development and operation. In plain terms: people need to know what “secure AI use” looks like in their day job, not just in a lab. 

Meanwhile, attackers are enjoying an upgrade. Generative tools make spear-phishing and social engineering more convincing and cheaper to run. Old cues—bad spelling, awkward phrasing—no longer help. Staff need sharper “verify-before-trust” habits and clear playbooks. 

What robust AI Security Awareness should cover 

1) Safe use fundamentals 

Demystify how models work, where they fail, and the risks from prompt leakage, sensitive inputs, and over-trusting outputs. Translate your data classification into simple guardrails for prompts and context. Tie this to DLP, access control and acceptable use so the message is consistent everywhere. 

2) Risky moments in real workflows 

Show staff where AI sits in their actual processes: summarising citizen queries, triaging incidents, drafting supplier letters, writing policy text, analysing images or logs. Treat AI outputs as untrusted by default, require basic provenance checks, and normalise human review for decisions with impact. 

3) Third-party and supply-chain exposure 

Your risk now includes vendors’ prompts, logs and model update cycles. Build AI into due diligence: where data goes, whether it trains models, how long it’s retained, what changes trigger notification, and how you exit cleanly. Reflect that in contracts, not just policy pages. 

4) Records, transparency and accountability 

Keep an audit trail of prompts, context and rationale so decisions can be explained and challenged. Make it discoverable for FOI and SARs. This isn’t bureaucracy; it’s how you defend decisions and prove you’re following your own rules. 

5) Sector-specific scenarios 

Government & CNI: handling sensitive operational data in prompts; insider risk; controlling model access; protecting incident communications from manipulation. 

Defence & MoD suppliers: Secure-by-Design habits; export controls and classification; data and model sovereignty; adversarial prompt injection and poisoning; supplier assurance. 

Policing & Local Government: privacy impact assessment, case data hygiene, transparent decision trails, and readiness for public scrutiny. 

6) Link to management systems 

Awareness should reinforce existing controls—ISO/IEC 27001 for information security, and any roadmap towards ISO/IEC 42001 for AI management—so training, policy and audit evidence line up neatly. 

How to make training change behaviour 

Role-based pathways. Differentiate content for policy owners, developers/analysts, and general staff. 

Scenario-led practice. Use your real use-cases and data classes, not generic examples. 

“Verify, then trust.” Build muscle memory for sanity checks, source validation and escalation. 

Crisp governance. Convert principles into short playbooks: what’s allowed, what’s not, and who to ask. 

Audit-friendly artefacts. Record completion, knowledge checks and attestation mapped to your risk register—fuel for internal audit and external assurance. 

Common pitfalls 

Policy theatre. A shiny document with no training collapses at the first deadline. 

One-and-done courses. Models, features and threats shift; plan refreshers. 

Shadow adoption. If secure tools are blocked or confusing, staff will route around them. Provide approved patterns and make them easy. 

Supply-chain blind spots. If your vendors’ AI isn’t in scope, your risk picture is a postcard, not a map. 

What Advent IM’s course adds – book into our course on the 11th of November 

Advent IM’s AI Security Awareness Training gives teams a pragmatic, policy-aligned foundation they can apply on day one. It’s designed for public sector, policing, Defence suppliers and private enterprises with sensitive or regulated workloads. The course connects secure AI use to governance, auditability and assurance, and can be integrated alongside ISO/IEC 42001 support so you’re not maintaining parallel processes. 

Expect plain-English explanations, hands-on exercises, and take-away checklists that align to the UK guidance landscape rather than competing with it. If you already run ISO/IEC 27001 training, this slots in cleanly. 

Quick starter checklist for CISOs and SIROs 

• Map AI use-cases and data classes; define where AI is permitted and under what conditions. 
• Decide your approved tools and patterns; document the “golden path” for common tasks.
• Update policies and NDAs to reflect AI data handling, logging and disclosure expectations. 
• Add AI requirements to supplier onboarding and contract renewals.
• Run targeted exercises: prompt hygiene, data redaction, output validation, incident simulation.
• Measure outcomes: adoption of approved patterns, reduction in high-risk prompts, time-to-escalate when something feels off.