Because approval delays become part of the attack surface. If attackers can identify weaknesses in minutes, then slow exception handling, manual sign-off, and broad standing privilege give them more time to exploit exposed systems before controls catch up.
Why This Matters for Security Teams
AI-assisted attacks compress the time between reconnaissance, credential abuse, and lateral movement, which means approvals are no longer a back-office workflow issue. They are a control that can either slow an attacker or give them a longer window to operate. IAM teams now have to treat approval latency, exception handling, and standing access as measurable risk factors, not just process inconveniences.
This is especially important where privileged access, service accounts, and agentic tooling overlap. Attackers do not need to “defeat” approval governance if they can route around it by exploiting overbroad entitlements or stale exceptions. That is why NHI governance and IAM governance increasingly converge. NHIMG’s The 2024 Non-Human Identity Security Report found that 88.5% of organisations say their non-human IAM practices lag behind or merely match human IAM maturity, which helps explain why approvals often lag behind the speed of AI-enabled abuse.
For threat context, the attack patterns described in the Anthropic report on the first AI-orchestrated cyber espionage campaign show how quickly AI can accelerate operational decision-making for an attacker. In practice, many security teams discover approval weakness only after an exception has already been used to expand access, rather than through intentional governance testing.
How It Works in Practice
In a traditional IAM model, approvals are often designed for human-paced workflows: request, review, sign off, provision, and audit. AI-assisted attackers change that sequence because they can automate discovery of weak points, prioritize accounts with the easiest approval paths, and keep retrying until a control gap opens. The risk is not just unauthorized access, but the time it takes for a control to notice that an exception was granted too broadly or too quickly.
Operationally, teams should think in terms of approval design patterns:
- Require stronger approval for privileged, non-human, and cross-environment access than for routine requests.
- Use just-in-time access and expiry by default so approvals do not become standing privilege.
- Bind approvals to context such as workload, destination, business justification, and time window.
- Log who approved what, when, and under which conditions for later detection and review.
This also applies to agentic systems that request secrets or invoke tools. If an AI agent can request elevated access on behalf of a workflow, approval logic needs to account for machine identity, not just the person who triggered the workflow. The OWASP NHI Top 10 is useful here because it helps teams examine how autonomous application behaviour changes access risk.
For control mapping, NIST SP 800-53 Rev 5 Security and Privacy Controls supports tighter authorization, least privilege, and auditability expectations, while MITRE ATT&CK Enterprise Matrix helps security teams reason about how valid accounts and privilege abuse show up in real intrusion paths. These controls tend to break down when approval chains are manual, exceptions are permanent, and service accounts are excluded from normal review cycles because those conditions create durable attacker windows.
Common Variations and Edge Cases
Tighter approval controls often increase operational overhead, requiring organisations to balance speed for legitimate work against abuse resistance for high-risk access. There is no universal standard for this yet, especially where AI agents, ephemeral workloads, and multi-cloud infrastructure collide.
One common edge case is emergency access. Best practice is evolving toward time-bound break-glass approvals with compensating monitoring, but many organisations still rely on broad standing privilege “just in case.” Another case is delegated approval in large engineering environments, where team leads approve access so quickly that the process becomes ceremonial. In those settings, the approval is technically present but functionally ineffective.
AI-assisted abuse also changes how teams should think about exceptions. If a request can be generated, modified, and resubmitted at machine speed, then exception queues need tighter thresholds, anomaly detection, and stronger correlation with identity posture. NHIMG’s 52 NHI Breaches Analysis is useful for understanding how identity sprawl and weak governance create repeatable failure modes. Where approvals cover cloud keys, API tokens, or workload credentials, the issue is often less about who requested access and more about whether the access can be justified, bounded, and revoked fast enough.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 and NIST SP 800-53 Rev 5 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.AC-4 | Approval delays affect least-privilege access governance and entitlement review. |
| NIST SP 800-53 Rev 5 | AC-2 | Account management controls support approval, provisioning, and revocation discipline. |
| OWASP Non-Human Identity Top 10 | Non-human identity abuse often exploits weak or slow approval workflows. | |
| OWASP Agentic AI Top 10 | Agentic systems can request and abuse access at machine speed. | |
| MITRE ATT&CK | T1078 | Valid account abuse is a common path once approval weaknesses are exposed. |
Tighten approval paths so privileged access is granted only with documented, time-bound justification.
