A living inventory that connects each AI agent to the tools, identities, data sources, and environments it can reach. It turns an opaque agent estate into something security teams can govern, review, and audit. For autonomous systems, the graph must capture runtime relationships, not just deployment metadata.
Expanded Definition
An agentic asset graph is more than an inventory of deployed AI agents. It is an operational map of how each agent relates to its identities, secrets, tools, APIs, data sources, policies, and runtime environments. In NHI security, that runtime view matters because an agent’s effective blast radius is defined by what it can reach and act on, not just where it was deployed. This makes the concept adjacent to asset management, IAM, and entitlement review, but distinct from all three because it focuses on autonomous execution paths.
Definitions vary across vendors on whether the graph should include only authenticated relationships or also inferred tool use, delegated access, and ephemeral session paths. For governance purposes, NHI Management Group treats the runtime relationship set as the source of truth, especially when applied alongside OWASP Agentic AI Top 10 and the NIST AI Risk Management Framework. The most common misapplication is treating the graph as a static CMDB view, which occurs when teams omit transient permissions, delegated credentials, and live tool invocations.
Examples and Use Cases
Implementing an agentic asset graph rigorously often introduces coverage and telemetry overhead, requiring organisations to weigh better control visibility against the cost of collecting and normalising runtime signals.
- An internal coding agent is mapped to the repositories, package registries, and production deployment tools it can invoke, so security teams can spot overbroad access before a release pipeline is abused.
- A customer support agent is linked to CRM records, ticketing systems, and knowledge bases, making it possible to distinguish intended service actions from data overreach, as discussed in AI Agents: The New Attack Surface report.
- A finance workflow agent is tied to payment APIs and approval systems, then reviewed against runtime privilege paths rather than only its configured role at deployment time.
- An agent using federated credentials is tracked through every secret, token, and downstream service account it can assume, which aligns with guidance in the CSA MAESTRO agentic AI threat modeling framework.
- After a prompt-injection incident, teams compare expected versus actual tool access to understand how the agent moved from benign assistance to unintended action, a pattern covered in Gemini AI Breach — Google Calendar Prompt Injection.
Why It Matters in NHI Security
Agentic systems fail safely only when their reachable assets are understood. Without a graph, security teams often discover excess access after an agent reads sensitive data, calls an unapproved tool, or propagates a compromised secret. That is why the graph is central to least privilege, secret governance, and incident response for NHIs. In the field, the risk is not theoretical: NHIMG research on AI Agents: The New Attack Surface report found that only 52% of companies can track and audit the data their AI agents access, leaving 48% with a blind spot for compliance and breach investigation. The same gap appears in credential abuse cases described in LLMjacking: How Attackers Hijack AI Using Compromised NHIs, where exposed access can be abused within minutes.
Practitioners should also align the graph with control objectives in the MITRE ATLAS adversarial AI threat matrix and NIST AI Risk Management Framework so that access paths, tool dependencies, and escalation routes are reviewable. Organisations typically encounter the need for an agentic asset graph only after an agent has already accessed something it should not have, at which point the graph becomes operationally unavoidable to address.
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 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF, NIST CSF 2.0 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 | Agent graphs expose secret and tool sprawl across non-human identities. |
| OWASP Agentic AI Top 10 | A2 | Agentic app guidance focuses on unintended tool use and excessive autonomy. |
| NIST AI RMF | AI RMF calls for mapping AI system context, dependencies, and risks. | |
| NIST CSF 2.0 | PR.AC-4 | Least privilege requires knowing which assets an identity can actually reach. |
| NIST Zero Trust (SP 800-207) | PA | Zero trust depends on continuous evaluation of subject, asset, and policy context. |
Reassess agent access at runtime and treat each tool call as a fresh authorization decision.
