They should define shared ownership for the identities that touch AI data, model inputs, and restoration workflows. IAM owns entitlement design and lifecycle controls, while recovery teams must verify that those same identities can support restore, rollback, and isolation needs. If either team works alone, the programme will miss dependencies that only show up during disruption.
Why This Matters for Security Teams
AI workloads change the accountability model because the identities involved are not limited to human users. Model runners, retrieval services, orchestration layers, backup jobs, and recovery tooling all depend on machine identities that can read data, invoke APIs, and trigger restoration steps. IAM often owns the entitlement model, but recovery teams are the first to discover whether those same identities can still function during rollback, isolation, or failover. That dependency is easy to miss until an incident exposes it.
This is why current guidance increasingly treats restoration access as part of identity governance, not a separate operational concern. The NIST Cybersecurity Framework 2.0 reinforces shared ownership across governance, protection, and recovery outcomes, while NHIMG research on Non-Human Identities shows why machine identities must be managed as first-class assets. In practice, many security teams encounter broken restore paths only after an outage has already forced them to test the accounts nobody reviewed during design.
How It Works in Practice
Shared accountability works best when IAM and recovery teams define a joint control boundary around the identities that touch AI data, model inputs, and recovery workflows. IAM should own entitlement design, approval logic, secret lifecycle, and revocation. Recovery teams should validate that those identities can perform the minimum tasks needed for restore, rollback, quarantine, and integrity checks without gaining broad production access.
A practical operating model usually includes:
- an inventory of every AI-related workload identity, including training, inference, RAG, backup, and admin automation paths
- policy definitions for restore-only access, so recovery actions are possible without permanent standing privilege
- time-bound credentials or workload identities for tasks that should exist only during a recovery window
- pre-approved break-glass paths with logging, approval, and revocation requirements
- joint testing of restore, rollback, and isolation scenarios in non-production and incident simulations
For implementation, SPIFFE workload identity specification is useful because it shifts the discussion from shared secrets to cryptographic workload identity. That matters for AI systems, where restore automation may need to authenticate services dynamically rather than rely on long-lived credentials. NHIMG’s Guide to SPIFFE and SPIRE is a practical reference for teams trying to separate service identity from static secrets and reduce recovery-time friction. Recovery controls should also be mapped to NIST SP 800-53 Rev 5 Security and Privacy Controls so the restore path is auditable, least-privilege, and repeatable.
These controls tend to break down when recovery tooling is built as a separate admin domain with its own unmanaged credentials, because restore success then depends on undocumented privilege exceptions rather than verified identity design.
Common Variations and Edge Cases
Tighter recovery access often increases operational overhead, requiring organisations to balance restore speed against privilege reduction and auditability. That tradeoff is especially visible for AI platforms that support model rollback, vector database recovery, or isolated rehydration of training data.
There is no universal standard for exactly who signs off on AI workload recovery identities yet, but current guidance suggests the accountability split should follow function rather than org chart. IAM should remain responsible for who can authenticate and what they can do. Recovery teams should be responsible for proving that those identities actually work when systems are degraded, segmented, or rebuilt.
Edge cases include third-party managed AI platforms, ephemeral notebook environments, and multi-region failover setups. In those environments, recovery teams may depend on vendor-managed access paths or federated identities that IAM does not directly administer. That is where joint runbooks matter most: they document which identities are trusted, which are disposable, and which must be recreated during restoration. NHIMG’s State of Secrets in AppSec is a reminder that static credentials and fragmented secrets management increase the odds of recovery failures, while the LLMjacking research shows how quickly exposed credentials can be abused if recovery accounts are left overly broad.
Best practice is evolving toward shared control testing, not shared blame after an incident. The teams that define restoration identities up front usually discover fewer surprises than those that assume backup access will “just work” when needed.
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 CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Covers lifecycle governance for non-human identities used in AI and recovery workflows. |
| OWASP Agentic AI Top 10 | A-03 | Agentic workloads need identity controls that remain safe during autonomous actions and recovery. |
| CSA MAESTRO | M1 | Defines governance and operational control for agentic AI security responsibilities. |
| NIST AI RMF | GOVERN | Shared accountability is a governance issue for AI risk management. |
| NIST CSF 2.0 | RC.RP-1 | Recovery planning must include identity dependencies for restore and rollback. |
Treat AI agents as governed workloads and test their access paths under restore and isolation conditions.
