Look for fewer conflicting interpretations of AI risk, clearer approval ownership, and better-quality exception decisions. If risk, legal, data, and security teams use different definitions, literacy has not translated into control. Good programmes produce consistent policy application and evidence that decision-makers understand the systems they govern.
Why This Matters for Security Teams
AI literacy only matters when it changes how decisions get made under pressure. If risk, legal, data, and security teams can describe AI systems but still approve them with conflicting assumptions, the programme is cosmetic. The real test is whether people can recognise model risk, data exposure, and control gaps early enough to prevent unsafe deployment or weak exceptions.
That is especially important in an environment where credential abuse and exposed secrets can turn AI tools into an access path for attackers, as shown in the DeepSeek breach and other NHI incidents documented by NHI Management Group. Teams also need a shared operating model, not just awareness content, which is why NIST Cybersecurity Framework 2.0 remains useful for tying literacy to governance outcomes.
In practice, many security teams discover poor AI literacy only after an exception has already been approved, rather than through intentional control testing.
How It Works in Practice
Security teams can measure AI literacy by looking for evidence that people make consistent, defensible decisions about AI systems. The first signal is whether reviewers use the same definitions for data use, model output, prompt handling, human oversight, and acceptable risk. The second is whether approvals contain specific control reasoning instead of vague statements like “accepted by leadership.”
A practical approach is to test literacy in workflow, not in theory:
- Review a sample of AI risk assessments and check whether owners identify the same control failures.
- Compare exception decisions across teams to see whether similar systems receive similar treatment.
- Ask approvers to explain why a system is allowed, restricted, or rejected, then check whether the answer matches policy.
- Look for evidence that legal, security, and data governance understand the system’s training data, usage boundaries, and escalation path.
This is where current guidance suggests pairing awareness with control validation. The The State of Non-Human Identity Security research shows how weak visibility, over-privilege, and poor rotation create real exposure, which is why literacy has to translate into concrete control decisions. NIST’s NIST Cybersecurity Framework 2.0 can help structure those checks around governance, identification, protection, and response.
One useful indicator is exception quality: good teams document the compensating control, the risk owner, the expiry date, and the review trigger. Poor teams approve AI use based on familiarity alone. These controls tend to break down when multiple departments share responsibility for the same model but no single owner can explain the operational risk.
Common Variations and Edge Cases
Tighter AI governance often increases review time, so organisations have to balance speed against decision quality. That tradeoff becomes visible in fast-moving environments where product teams want broad experimentation and security teams want narrow approvals.
There is no universal standard for measuring AI literacy yet, so best practice is evolving. Some organisations focus on training completion, but that is a weak indicator unless it is backed by consistent policy application. Others track tabletop results, exception trends, or the percentage of decisions that require rework. Those are better signals because they show whether people can apply judgement, not just recall terminology.
Edge cases matter. A team may score well on basic AI awareness while still failing on procurement, data lineage, or vendor oversight. Another team may know the risks but still escalate every decision because ownership is unclear. In both cases, the programme looks active but does not create control maturity. The strongest evidence is repeated, low-variance decision-making across similar cases, especially where the system touches secrets, customer data, or external integrations.
For deeper context on the operational side of AI exposure, NHI Management Group research on DeepSeek breach and the broader The State of Non-Human Identity Security findings both show that risk awareness only becomes meaningful when it changes control behaviour.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RR-01 | AI literacy is working when roles and owners make consistent governance decisions. |
| NIST AI RMF | GOVERN | Measures whether AI understanding is translating into accountable risk governance. |
| OWASP Agentic AI Top 10 | A1 | Shared understanding reduces unsafe agentic use and inconsistent approval decisions. |
Use governance routines to test if teams can explain AI risk, approve controls, and justify exceptions.
Related resources from NHI Mgmt Group
- How can security teams tell whether NHI governance is actually working?
- How can security teams tell whether endpoint privilege management is actually working?
- How can security and data teams tell whether a marketplace is actually working?
- How can security teams know whether endpoint policy enforcement is actually working?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 23, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
