The practice of selecting, curating, and delivering the information an AI system uses at runtime. In agentic environments, context engineering is a security function because the quality, provenance, and trust level of the inputs directly shape the system’s actions and outputs.
Expanded Definition
Context engineering is the disciplined practice of deciding what an AI system can see at runtime, how that information is packaged, and which sources are trusted enough to influence action. In agentic environments, it sits between model capability and operational control, because the context window becomes a security boundary as much as a functional one. The term is still evolving across vendors and research communities, so usage varies: some teams treat it as prompt design, while others include retrieval policies, tool outputs, memory management, and policy filters. NIST Cybersecurity Framework 2.0 provides a useful governance lens for controlling information flows, but it does not define context engineering as a standalone control domain.
For NHI and agentic AI programs, context engineering is not only about relevance. It is about provenance, freshness, access scope, and whether secrets, credentials, or privileged instructions are allowed to enter the model’s decision path. That is why NHI Management Group treats it as a security function, not just an AI tuning exercise. The most common misapplication is assuming that any retrieved data is safe to expose to an agent, which occurs when teams omit source validation and privilege filtering from runtime context assembly.
Examples and Use Cases
Implementing context engineering rigorously often introduces latency and governance overhead, requiring organisations to weigh better task accuracy against tighter source control and slower retrieval pipelines.
- An internal support agent is limited to approved knowledge base articles, while ticket comments and user-uploaded files are excluded unless they pass a trust check.
- A software agent receives only short-lived, task-specific instructions and no reusable API keys, reducing the chance that runtime context can be replayed outside the workflow.
- A finance copilot is given policy excerpts and current approval thresholds, but it is blocked from ingesting raw payroll exports unless a human authorizes the request.
- A retrieval layer uses document provenance labels so that the model can distinguish authoritative system records from drafts, transcribed notes, or third-party summaries.
- An autonomous remediation agent consults the Ultimate Guide to NHIs to align runtime access decisions with NHI lifecycle governance, then cross-checks policy expectations against NIST Cybersecurity Framework 2.0.
In practice, context engineering also includes retrieval-augmented generation safeguards, prompt-injection resistance, and memory scoping so that the agent does not confuse transient conversation state with durable authority. NHI Management Group’s research shows that only 5.7% of organisations have full visibility into their service accounts, which makes runtime trust decisions especially difficult when agents draw from identities and systems that are poorly mapped. The same governance challenge appears in source curation: if the pipeline cannot explain why a record was included, it cannot reliably defend why the model acted on it.
Why It Matters in NHI Security
Context engineering matters because agentic systems often fail through contamination rather than code execution. When an agent is handed the wrong runtime context, it may expose secrets, overstep its intended authority, or act on stale instructions that no longer match policy. That makes context a direct control surface for NHI security, especially where service accounts, tokens, and machine-to-machine approvals are involved. NHI Management Group reports that 79% of organisations have experienced secrets leaks, with 77% of those incidents causing tangible damage, and 96% store secrets outside secrets managers in vulnerable locations including code, config files, and CI/CD tools. Those conditions amplify the risk that an agent will ingest sensitive material it should never have seen.
Good context engineering therefore supports least privilege, source integrity, and operational containment. It helps separate what an agent may know from what it may use, which is critical when automating access review, incident response, or software delivery. It also aligns with zero trust thinking by forcing every runtime input to earn its place in the decision path. Organisations typically encounter the need for context engineering only after an agent exposes a secret, follows a poisoned instruction, or produces an unauthorized action, at which point context 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 Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | Agentic AI guidance centers on prompt, tool, and context risks. | |
| OWASP Non-Human Identity Top 10 | NHI-02 | Runtime context often exposes secrets and privileged NHI material. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access maps to controlling what context an AI can consume. |
Filter secrets and privileged data out of agent context unless explicitly required and approved.
