The practice of deliberately limiting the information an AI system receives so it can complete a task without being overwhelmed by irrelevant data. In security and engineering workflows, it is a control pattern that reduces error, limits scope creep, and improves auditability.
Expanded Definition
Context curation is the intentional selection, trimming, and ordering of information supplied to an AI system so the system can complete a task with enough signal and minimal noise. In practice, it sits between raw data access and prompt engineering: prompt engineering shapes the request, while context curation shapes what the model is allowed to see. For NHI and agentic AI workflows, that distinction matters because tool-using agents often act on behalf of a human or service account and can amplify mistakes when they ingest irrelevant secrets, stale tickets, or unrestricted operational data. The security goal is not to hide everything, but to constrain context to what is necessary for the task, the role, and the current trust boundary. This aligns closely with governance ideas in NIST Cybersecurity Framework 2.0, especially around controlled access and risk-aware decision-making. Definitions vary across vendors when context curation is bundled with memory management, retrieval tuning, or prompt filtering, so NHIMG treats it as a distinct control pattern rather than a generic AI convenience feature. The most common misapplication is treating any reduction in prompt length as context curation, which occurs when teams remove text without first defining task scope, authority, and data sensitivity.
Examples and Use Cases
Implementing context curation rigorously often introduces a tradeoff between model convenience and operational restraint, requiring organisations to weigh richer context against lower exposure and better traceability.
- A security analyst asks an LLM to summarise an incident, but only the incident timeline, alert metadata, and approved case notes are provided, not unrelated customer records or secret-bearing attachments.
- An autonomous remediation agent receives a scoped subset of CMDB entries and change records before making a ticketing decision, reducing the chance of acting on outdated infrastructure context.
- A software engineering workflow uses curated repository snippets, current dependencies, and approved architecture notes instead of feeding the full codebase into the model, which improves relevance and reduces leakage risk.
- A privileged access review assistant is given only role assignments, business justification, and time-bound approvals, rather than broad identity history, helping keep the review focused and auditable.
- An internal knowledge assistant references a policy extraction layer that excludes secrets, token material, and obsolete procedures, so users get answerable context without exposing sensitive operational data.
Good practice is to pair context curation with source provenance, explicit retrieval rules, and a documented decision boundary for what the model may see. Where retrieval is involved, organisations should also align with NIST Cybersecurity Framework 2.0 principles for governance and protective controls, especially when AI output can affect operational access or control decisions.
Why It Matters for Security Teams
Security teams care about context curation because AI systems fail differently when their inputs are bloated, ambiguous, or contaminated. Overinclusive context can expose secrets, widen the blast radius of a prompt injection, and cause an agent to infer authority it never had. Underinclusive context can produce brittle decisions, but that is usually easier to detect and correct than silent overreach. For identity and NHI governance, context curation becomes especially important when an agent is allowed to reason over service account entitlements, credential metadata, or workflow state. The right question is not whether the model can know more, but whether it should. That is why context curation fits naturally alongside least privilege, segregation of duties, and controlled data handling in security programmes. It also supports auditability because reviewers can explain why the model saw a particular subset of information and why it was sufficient for the task. Teams that ignore context boundaries often discover the problem only after an agent surfaces an unauthorised field, misroutes a ticket, or acts on stale information, at which point context curation 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 CSF 2.0, NIST AI RMF and NIST AI 600-1 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.AC-4 | Context curation supports least-privilege access to only the information needed for the task. |
| NIST AI RMF | AI RMF addresses trustworthy AI governance where input scope and information quality shape risk. | |
| NIST AI 600-1 | The GenAI profile emphasizes managing model use, data handling, and operational risk. | |
| OWASP Non-Human Identity Top 10 | NHI guidance is relevant when agents or service identities consume curated operational context. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance covers unsafe tool use and overbroad context that can drive harmful actions. |
Limit AI-visible inputs to the minimum necessary and review them like any other access entitlement.
