How do cloud teams keep AI security from becoming another silo?

Why This Matters for Security Teams

Cloud teams are being asked to secure AI services without creating a separate control plane, yet that is exactly how silos form: one team owns cloud posture, another owns model access, and a third owns pipeline secrets. The result is duplicated approval paths, inconsistent revocation, and blind spots around which AI system can act on behalf of which workload. Guidance from the CSA MAESTRO agentic AI threat modeling framework and NHIMG’s reporting on NHI maturity gaps both point to the same operational problem: identity is the control point, not the model itself. NHIMG’s 2024 Non-Human Identity Security Report found that 88.5% of organisations say their non-human IAM practices lag behind or only match human IAM, which explains why AI governance often arrives as a patchwork of exceptions. In practice, many security teams encounter AI sprawl only after a cloud incident or secret exposure has already occurred, rather than through intentional governance design.

How It Works in Practice

Keeping AI security out of a silo means folding it into the same ownership, approval, and review process used for service accounts, workload identities, and cloud remediations. That starts with assigning each AI service a named business owner, a technical owner, and a workload identity that can be tracked across environments. The identity should be bound to the specific runtime, not to a person, and should authenticate with short-lived credentials instead of standing secrets wherever possible. Standards such as OWASP guidance for non-human identity risk and the SPIFFE workload identity model both reinforce this direction: prove what the workload is, then authorize what it can do at request time.

Operationally, that means integrating AI assets into existing cloud controls:

• Put AI services, agents, and automation jobs into the same asset inventory as cloud workloads.
• Review model API access, secret access, and infrastructure permissions in one entitlement workflow.
• Issue just-in-time credentials for discrete tasks, then revoke them automatically after completion.
• Evaluate access with context-aware policy at runtime, rather than relying only on static role mappings.
• Monitor AI actions through the same detection and audit channels used for other privileged cloud identities.

This matters because autonomous systems can chain tools, request new permissions, and repeat actions faster than a human reviewer can intervene. The NHIMG article on the Azure Key Vault privilege escalation exposure shows how secrets governance and cloud privilege can collapse into the same failure path when access is not tightly scoped. Best practice is evolving toward policy-as-code and runtime decisioning, but there is no universal standard for this yet. These controls tend to break down in multi-cloud environments with fragmented identity tooling because entitlement data, secret stores, and audit logs are rarely normalized across platforms.

Common Variations and Edge Cases

Tighter AI governance often increases operational overhead, requiring organisations to balance faster automation against more frequent access review and policy maintenance. That tradeoff becomes sharper when platform teams want to move quickly on cloud remediations while security teams want every AI action to be pre-approved. In mature environments, a single control plane can still work if it treats AI agents as another class of privileged workload; in less mature environments, teams sometimes create separate AI approval boards, which usually deepens the silo instead of reducing risk.

There is also a real distinction between predictive AI services and autonomous agentic workflows. Current guidance suggests predictive services can often be governed like other application workloads, while agents that can plan, chain tools, or alter infrastructure need stronger runtime checks, tighter JIT issuance, and more aggressive revocation. The Anthropic Project Glasswing work reflects how fast this governance area is evolving, but it does not yet establish a universal operating model. For cloud teams, the practical edge case is hybrid ownership: when a platform team deploys the agent, a security team approves the policy, and an application team owns the data path, the only workable answer is a shared identity and lifecycle review process. The Snowflake breach is a reminder that identity and access failures rarely stay isolated to one layer of the stack.

Related resources from NHI Mgmt Group

• How should security teams prioritise NHI remediation in cloud environments?
• How should security teams govern non-human identities in cloud environments?
• How should security teams handle risks from AI browser extensions?
• How should security teams govern API keys used for generative AI access?