What is AI Interaction Governance?
June 8, 2026
Enterprise AI risk is created at one moment: when a prompt leaves the agent, or when tools/call is about to run. Not when traffic crosses the firewall. Not when a file syncs to personal cloud storage an hour later.
A developer asks Cursor to debug a deployment. The agent reads .env, pulls a database connection string, and passes it into model context. The task completes. Nobody gets an alert. The secret is now in a provider request log and possibly in shared chat history.
That is an agent interaction. Until you govern those interactions at the boundary where they execute, every other control is upstream or downstream of where the damage actually happens.
The moment risk is created
Traditional stacks were built around channels: email, endpoints, cloud storage, sanctioned SaaS. AI workflows do not fit that shape. Engineers chain MCP tool calls in seconds. Models and clients change weekly. Secrets move through tool arguments the chat UI never shows.
The highest-risk decision in an agent workflow happens at interaction time — when a human or agent decides what to send, and when a tool is about to execute. Network DLP and CASB see traffic classes and app IDs. They rarely answer: what did this specific tool call contain, what did policy do, and can you prove it in an audit?
Non-Human Identity Management (NHIM) is the discipline of governing autonomous agents at that hop: evaluate each prompt and tool call against policy, apply an action, and produce metadata evidence tied to that decision. You may also see this called AI Interaction Governance (AIG) — same boundary, same problem; Blekline leads with NHIM in product copy because the control unit is the agent session, not a human user account.
Not DLP, not CASB, not human IAM
All layers matter. Blekline owns L4 — complementary to Kong at L3, Okta at L2, and CASB at L1. It is not a CASB replacement, not network DLP, and not a SOC 2 certification.
The interaction decision
For each prompt or tool call, policy returns one of three actions:
- Allow — proceed unchanged
- Mask — redact sensitive entities before the model or tool sees them
- Block — stop the interaction before execution
Each decision emits metadata: action, risk tier, tool name, client surface, correlation ID. Default configuration does not store full prompt or argument bodies — metadata-only audit, exportable to CSV and SIEM.
That trio — decision, evaluation context, outcome — is what makes NHIM auditable. You are not logging “someone used ChatGPT.” You are recording what policy decided at the agent hop and when.
Three boundaries, one enforcement point
Blekline implements NHIM through three runtime boundaries on the same policy engine:
Trust Vault (ingress). Stateful tokenization replaces PII and secrets with placeholders before models reason. Hydration stays in your VPC — raw secrets do not round-trip through the control plane.
Lineage Firewall (execution). Blocks destructive tool calls when session lineage is contaminated — for example, after prompt injection or cross-turn secret exposure.
Audit & SIEM (egress). Metadata-only activity stream. Not zero-retention marketing speak — metadata-only by default, with explicit export paths for security review.
Deploy as MCP/SDK in the IDE, as a Docker or Kubernetes sidecar for fleet policy, or both. The agent client does not need a rewrite; governance sits at the call boundary.
What to measure
NHIM programs should track governance metrics, not ticket volume alone:
- Policy coverage — share of in-scope agent interactions evaluated (MCP, SDK, sidecar)
- Prevented high-risk interactions — masked and blocked events, trended weekly
- Decision-to-evidence latency — time from enforce to auditable record
- Fleet posture — agent-hop gaps found by
nhim-auditvs remediated
If you cannot measure these, you have visibility, not governance.
Getting started (eval journey)
Most teams fail by blocking everything on day one. A practical sequence:
- Discover — run nhim-audit on a K8s cluster (no account) or connect MCP in Cursor for a single team —
https://app.blekline.com/docs/tools/nhim-audit - Simulate — policy against real patterns without blocking; see what would have been masked
- Enforce — mask secrets and PII; block high-risk tools (shell, arbitrary egress) with narrow exceptions
- Audit — export decision-linked metadata for DPO review and incident response
Start with one track: Track 03 MCP eval for engineering, Track 01 K8s fleet for platform teams. Expand when coverage metrics justify it.
NHIM overview — https://app.blekline.com/docs/introduction/nhim
Eval journey (Track 0→3) — https://app.blekline.com/docs/get-started/eval-journey
Trust boundaries & claims — https://app.blekline.com/docs/security/trust-boundaries
Runtime simulator — https://app.blekline.com/docs/playground/runtime-enforcement
