Static AI-BOMs miss the runtime gap attackers exploit in AI workloads

At a glance

What this is: This analysis argues that AI-BOMs become security controls only when tied to runtime telemetry, because static manifests cannot see behavioural drift or sanctioned tool misuse.

Why it matters: For IAM, NHI, and AI security teams, the key issue is whether inventories can expose how service accounts, API keys, and agent identities are actually used in production.

By the numbers:

• ARMO says its eBPF sensor collects telemetry with 1-2.5% CPU and approximately 1% memory overhead.

👉 Read ARMO's analysis of why static AI-BOMs fail in production

Context

AI-BOMs are meant to document the models, tools, data sources, and identities that make up an AI system. The governance problem is that a static manifest can only describe what was intended at deployment, not what the workload is doing once it is live.

That gap matters because AI agents, RAG pipelines, and model servers can change behaviour without any change to the inventory record. For IAM and NHI programmes, the intersection is direct: service accounts, API keys, and IAM roles become part of the AI attack surface, but only runtime visibility shows whether those identities are being used safely.

The article’s starting position is common in many enterprises: teams assume inventory equals control until production proves otherwise.

Key questions

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. It can show what was approved for deployment, but it cannot reveal prompt injection, tool misuse, behavioural drift, or the identities actually used during execution. That means investigations, containment, and blast-radius assessment all depend on other telemetry instead of the inventory itself.

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. At that point, the quality of identity assignment, privilege scope, logging, and lifecycle control determines whether the system can be governed and audited responsibly.

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. If the record is current, linked to runtime evidence, and triggers action when systems drift, it is functioning as a governance control rather than a static catalog.

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.

Technical breakdown

Why static AI-BOMs fail at runtime

A static AI-BOM is a point-in-time declaration, usually built from code, deployment files, or CI output. That makes it useful for governance baselines, but weak for security because AI workloads load tools, models, and data paths dynamically after deployment. The result is a coverage gap: the BOM may say an agent has three approved APIs, while the running system calls five. Security teams then see an inventory that is already stale before the incident starts. Runtime truth, not declared intent, is what determines whether a control can investigate or contain an event.

Practical implication: treat the AI-BOM as a baseline and require runtime validation before using it for response decisions.

How prompt injection and tool misuse bypass declared inventories

The most important failure mode is misuse, not addition. Prompt injection can steer an agent to use an approved tool with attacker-controlled parameters, so the component list still looks compliant while the behaviour is malicious. This is especially relevant where the AI system has sanctioned access to internal APIs, vector databases, or external services through service accounts and API keys. The manifest records that the tool exists, but not whether the tool was used in a way that crosses a trust boundary. That is why application-layer telemetry is essential for AI workloads.

Practical implication: correlate tool invocation, parameter patterns, and identity context instead of reviewing approved integrations only.

Why behavioral baselines matter more than component lists

A runtime-connected AI-BOM combines inventory with behavioural baselines, so the system can detect drift. Drift means a known workload starts acting differently, such as a model server making new network connections or a RAG pipeline touching unusual data volumes. This is different from detecting an unknown component. It is a behavioural control problem, and it maps closely to NIST AI Risk Management Framework post-deployment monitoring. Without baselines, security teams can neither distinguish normal variability from compromise nor produce evidence that an AI control remained effective in production.

Practical implication: build baselines from observed production behaviour and alert on deviations that affect data access or outbound communications.

Threat narrative

Attacker objective: The attacker wants to abuse an approved AI workflow to access data or trigger actions while remaining invisible to manifest-only controls.

1. Entry occurs when an attacker uses prompt injection or a similar manipulation to influence an AI agent already running inside an approved workflow.
2. Escalation happens when the agent invokes sanctioned tools, APIs, or data sources with attacker-controlled parameters, turning legitimate access into abuse.
3. Impact follows when the manipulated workflow reaches internal data, external endpoints, or downstream systems and exfiltrates or alters information without changing the declared inventory.

NHI Mgmt Group analysis

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.

Runtime-connected inventory is the real control boundary for AI workloads. The article makes the strongest case not for more paperwork, but for continuous validation tied to telemetry. This is where NHIMG’s identity lens matters: AI agents are only governable when the identities they use, including service accounts and API keys, are visible in motion. For AI governance, the question is no longer whether a system was approved, but whether its runtime identity behaviour stayed inside its authorised boundary.

Declared dependencies miss the most dangerous AI threat pattern: sanctioned misuse. Prompt injection, tool misuse, and AI-mediated exfiltration do not require an attacker to introduce a new component. They exploit the gap between what is listed and what is happening. That gap is a form of governance debt, because teams accumulate inventories without the validation layer needed to make them meaningful. Practitioners should read this as a warning that compliance evidence and security evidence are diverging.

AI-BOMs now sit at the intersection of AI governance and identity governance. The article is fundamentally about observability, but its operational consequences are identity-centric: who or what is allowed to call tools, access data, and use secrets in production. That makes AI-BOMs relevant to IAM, PAM, NHI, and AI security teams at the same time. The practical conclusion is that AI inventory programmes must be built around workload identity, not just model documentation.

AI governance debt: This article exposes the cost of letting AI inventories remain static while production behaviour changes underneath them. That debt accumulates when discovery, telemetry, and response are not linked, and the result is a control that looks complete on paper but cannot answer incident questions. Practitioners should treat this as a signal to align AI inventory with enforcement and response.

What this signals

The operational signal for practitioners is that AI security is moving from asset lists to enforced runtime evidence. Once agents can dynamically invoke tools, the programme question becomes whether the identity layer can prove what happened, not just what was approved. That is why runtime validation, behavioural baselines, and incident enrichment need to sit together in the same control path.

Runtime inventory gap: this is the point where declared AI dependencies stop being trustworthy and observable behaviour becomes the only defensible source of truth. Teams should expect future AI governance reviews to ask for runtime evidence tied to identities, not just manifests. For the identity dimension of that shift, the NHI lifecycle view in NHI Lifecycle Management Guide is the better operating model.

For practitioners

• 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.
• Connect AI-BOM data to incident response workflows Ensure the inventory can enrich alerts with execution chains, affected data sources, and involved identities so SOC analysts can contain an AI workload without manual ticket chasing.

Key takeaways

• Static AI-BOMs create governance comfort, but they do not prove how AI workloads behave in production.
• The meaningful control problem is runtime drift, sanctioned tool misuse, and identity-backed access inside AI workflows.
• Security teams should connect AI inventories to telemetry, workload identity, and incident response if they want the BOM to function as a control.

Key terms

• AI-BOM: An AI bill of materials is a structured inventory of the components that define an AI agent, including the model, prompt, tools, retrieval sources, and dependencies. In practice, it is the evidence base for review, change control, and risk assessment when the agent evolves after deployment.
• Runtime-Derived AI-BOM: An inventory of the models, tools, adapters, and components that actually loaded and ran in an AI workload. Unlike a declared bill of materials, it reflects live system state, which is essential when components appear only at runtime or are pulled dynamically from external sources.
• Behavioural Drift: Behavioural drift is the gradual change in what an identity does compared with what it was originally approved to do. For AI agents, drift can come from prompt changes, model updates, expanded integrations, or altered workflows, which makes access review alone an incomplete control.
• Prompt Injection (Agentic): An attack where malicious instructions are embedded in content that an AI agent reads — causing the agent to execute unintended actions using its own legitimate credentials. A primary vector for agent goal hijacking and identity abuse.

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

👉 The full ARMO post covers runtime telemetry, AI workload baselines, and incident response context in more implementation detail.

Deepen your knowledge

The NHI Foundation Level course, the industry's only accredited NHI security programme, covers NHI governance, machine identity security, and secrets management. It helps practitioners connect identity controls to the broader security programme they run across AI and cloud workloads.