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20/08/2026 6:09 pm
TL;DR: Static AI-BOMs create false confidence because they document intended AI components, not the APIs, data paths, and identities an agent uses in production, according to ARMO. The real security gap is runtime divergence, where prompt injection, tool misuse, and data exfiltration happen inside approved workflows and evade manifest-only governance.
NHIMG editorial — based on content published by ARMO: What is an AI-BOM? Why Static Manifests Fall Short
By the numbers:
- ARMO says its eBPF sensor collects telemetry with 1-2.5% CPU and approximately 1% memory overhead.
Questions worth separating out
Q: What breaks when an AI-BOM is only a static manifest?
A: A static AI-BOM breaks at the point where security teams need runtime truth.
Q: When does AI governance become an IAM and NHI problem?
A: It becomes an IAM and NHI problem as soon as autonomous systems use credentials, APIs, or delegated access to perform actions.
Q: How do security teams know if an AI inventory is actually working?
A: An AI inventory is working when it is used to make real decisions about approval, access, risk rating, and review cadence.
Practitioner guidance
- Instrument runtime telemetry for AI workloads Deploy low-level telemetry across clusters hosting AI agents so you can observe processes, network connections, syscalls, and API calls instead of relying on deployment manifests alone.
- Link AI inventories to workload identities Map models, tools, vector stores, and APIs to the service accounts, API keys, and IAM roles that use them, then review those identities as part of AI governance.
- Build behavioral baselines from production activity Record normal API patterns, data access volumes, and outbound destinations for each AI component, then alert on deviations that indicate misuse or drift.
What's in the full article
ARMO's full blog covers the operational detail this post intentionally leaves for the source:
- eBPF telemetry collection details for AI workloads running in Kubernetes
- Application Profile DNA baseline behaviour examples for agents, tools, and data access
- Incident enrichment workflow mechanics for SOC triage and containment
- Step-by-step runtime-to-governance mapping for production AI inventory
👉 Read ARMO's analysis of why static AI-BOMs fail in production →
AI-BOM runtime visibility: are your controls keeping up?
Explore further
20/08/2026 6:45 pm
Static AI-BOMs are governance artifacts, not security controls. They record declared intent, which is useful for audit and procurement, but they do not prove what an AI workload actually did in production. The critical failure mode is the assumption that inventory completeness equals operational trust. That assumption collapses as soon as an agent can call tools dynamically, and security teams should treat any manifest-only approach as evidence of documentation maturity, not control maturity.
A question worth separating out:
Q: Should organisations use AI-BOMs for compliance or response first?
A: They should use AI-BOMs for both, but response should be the test of whether compliance is real. A manifest that cannot enrich an alert or help isolate a compromised workload is weak evidence of control effectiveness, even if it satisfies a governance checklist.
👉 Read our full editorial: Static AI-BOMs miss the runtime gap attackers exploit in AI workloads
