Facts an investigation depends on, such as who a user is, what an asset does, how data fields are structured, and where authoritative records live. In an AI SOC, declarative knowledge lets the system interpret signals correctly and ask the environment for the right information.
Expanded Definition
Declarative knowledge is the static, referenceable knowledge an investigation depends on to interpret events correctly. In security operations, it includes facts about identity, assets, data schemas, business context, and authoritative sources. It is different from procedural knowledge, which describes how to do something, and from implicit pattern recognition, which may infer meaning without stating the underlying facts. For AI-enabled security tooling, declarative knowledge is what lets an agent or analyst determine whether a login is normal, whether a process is expected, or whether a record is authoritative before taking action. NIST does not define the term as a standalone control concept, but the idea aligns closely with the NIST Cybersecurity Framework 2.0 emphasis on governed asset, identity, and data context.
Definitions vary across vendors when declarative knowledge is discussed in AI, analytics, or automation platforms. In some products it is treated as metadata, in others as a knowledge graph, and in agentic workflows it may also include tool schemas and policy facts. The most common misapplication is treating loosely inferred or stale context as declarative knowledge, which occurs when teams fail to tie the investigation source to authoritative records.
Examples and Use Cases
Implementing declarative knowledge rigorously often introduces curation overhead, requiring organisations to weigh better decision quality against the cost of maintaining trusted source data.
- In an AI SOC, an agent uses declarative knowledge about user roles, asset ownership, and approved services to decide whether a spike in API calls is suspicious or expected.
- A detection rule references declarative knowledge about a cloud account’s purpose, so an automated alert can distinguish a build pipeline from an unapproved admin session.
- A case-management workflow consults declarative knowledge about data field structure to avoid misclassifying an empty value as missing evidence rather than a valid null state.
- An identity investigation uses declarative knowledge from authoritative directories and HR systems to verify who a person is and which entitlements should exist.
- An AI agent relies on declarative knowledge of tool permissions and data classifications before invoking a source, reducing the chance of unsafe retrieval or overbroad action.
This concept is especially important where the environment is dynamic and the facts must be validated before any response is triggered. Guidance from the NIST Cybersecurity Framework 2.0 reinforces the need to know what assets, identities, and data are in scope before control decisions are made.
Why It Matters for Security Teams
Security teams need declarative knowledge because investigation quality depends on factual context, not just event volume. Without it, analysts and AI systems can misread benign activity as malicious, miss identity anomalies, or take action against the wrong asset. In NHI and agentic AI environments, the risk is sharper: an autonomous entity may have execution authority, but it still needs accurate facts about ownership, allowed tools, trust boundaries, and source-of-truth records before it acts. Declarative knowledge therefore supports safer detection, triage, and response by grounding decisions in governed context rather than guesswork. It also matters for auditability, because reviewers need to see which factual sources informed a decision and whether those sources were current.
Where this intersects with identity and access, declarative knowledge helps confirm who should have access, what level of assurance is required, and which records are authoritative under frameworks such as NIST Cybersecurity Framework 2.0. Organisations typically encounter the operational cost of missing declarative knowledge only after an investigation stalls, an AI agent escalates the wrong incident, or a response action is reversed because the underlying facts were wrong.
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 CSF 2.0, NIST AI RMF and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OV-01 | CSF 2.0 governance depends on authoritative context for risk decisions and oversight. |
| OWASP Non-Human Identity Top 10 | NHI guidance depends on knowing authoritative identity, ownership, and secret context. | |
| OWASP Agentic AI Top 10 | Agentic systems need factual context before tool use, retrieval, or autonomous action. | |
| NIST AI RMF | AI RMF calls for context, governance, and reliability in AI system use and decisions. | |
| NIST SP 800-63 | IAL2 | Identity assurance depends on reliable evidence about who a subject is. |
Maintain trusted factual context for assets, identities, and data before making control decisions.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on August 26, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
