Human-centered AI is an operating approach in which the person remains responsible for the decision and the machine provides context, prioritisation, or recommendation. The goal is not to replace judgement, but to make security decisions more consistent, traceable, and defensible.
Expanded Definition
Human-centered AI is not a handoff of authority to an automated system. It is a governance pattern where the person retains accountability while the machine contributes analysis, ranking, anomaly detection, or recommendation. In NHI and IAM operations, that distinction matters because the system may surface risk, but a human must approve a privilege change, a secret rotation decision, or an incident response action.
The model aligns well with NIST Cybersecurity Framework 2.0, especially where organisations need repeatable decisions without surrendering control. Definitions vary across vendors when AI is described as “autonomous” or “copilot-like,” so the practical test is whether a person can still override the recommendation and is accountable for the outcome. This is especially relevant in environments using agents, service accounts, and secrets that can trigger real-world actions. NHI Management Group treats the term as an operating discipline, not a marketing label.
The most common misapplication is treating a recommendation engine as human-centered when the human merely clicks through a default suggestion without meaningful review.
Examples and Use Cases
Implementing human-centered AI rigorously often introduces workflow friction, requiring organisations to weigh faster triage against the cost of additional review steps and auditable approval paths.
- A security analyst receives AI-ranked alerts for leaked API keys, then validates context before triggering rotation or revocation.
- An IAM team uses AI to prioritise stale service accounts, but a human approves which identities are disabled after business impact review.
- During investigation of compromised credentials, analysts compare AI-surfaced patterns with evidence from the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research before taking containment action.
- A developer platform recommends secret-scanning remediation order, while a platform owner decides whether to block deploys or allow temporary exceptions.
- Teams reviewing training-data exposure use the DeepSeek breach as a case study for why AI-assisted review still needs human approval when secrets are involved.
For implementation guidance, practitioners can also map review workflows against identity assurance and access governance expectations in the broader NIST identity ecosystem, even when the AI itself is only advisory.
Why It Matters in NHI Security
Human-centered AI reduces the chance that AI output becomes an unchallenged authority over secrets, service accounts, or agent permissions. That matters because NHI incidents often start with overload, inconsistent review, or misplaced trust in machine-generated prioritisation. In The State of Secrets in AppSec, GitGuardian and CyberArk report that only 44% of developers follow security best practices for secrets management, which shows how easily human process gaps can undermine even well-designed tooling.
Used correctly, the approach creates traceability: who reviewed the recommendation, what evidence supported the action, and why the final decision was accepted or rejected. That makes it valuable for investigations, privileged access changes, and incident response where AI can speed analysis but not absorb accountability. The pattern also helps leaders govern agentic systems by keeping execution authority bounded and auditable through human oversight, rather than granting silent autonomy to workflows that can touch production identities and secrets.
Organisations typically encounter the limits of this model only after an AI-assisted recommendation causes an access mistake or a delayed response, at which point human accountability becomes operationally unavoidable to address.
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 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | AGENT-03 | Human oversight limits unsafe agent autonomy and keeps decisions reviewable. |
| OWASP Non-Human Identity Top 10 | NHI-01 | Human review reduces misuse of NHI-driven decisions and hidden privilege changes. |
| NIST CSF 2.0 | GV.OV-01 | Governance calls for oversight of automated security decisions and their outcomes. |
Set decision authority, review checkpoints, and exception handling for AI-assisted security workflows.
