What is AI Interaction Governance?
Enterprise AI risk is created at a specific moment: when someone sends a prompt, or when an agent calls a tool. Not when traffic hits the network. Not when a file leaves a shared drive three hours later.
A developer asks Cursor to debug a deployment. The agent reads a repo, pulls a connection string from .env, and passes it into a model API call as context. The task completes. Nobody gets an alert. The secret is now in a provider's request log and possibly in shared conversation history.
That is an interaction. And until you govern interactions at that boundary, every other control is upstream or downstream of where the damage actually happens.
The moment risk is created
Traditional security stacks were built around channels: email, endpoints, cloud storage, sanctioned SaaS apps. AI workflows don't fit that shape. Users paste customer data into a browser tab. Agents chain ten tool calls in a minute. Models change weekly.
The highest-risk decision in an AI workflow happens at interaction time - when a human or agent decides what to send, and when a tool is about to execute. Controls that only watch the network perimeter or audit after the fact miss that window, or arrive too late to prevent it.
AI Interaction Governance (AIG) is the discipline of governing those moments: evaluating each interaction against policy, applying an action, and producing evidence tied to that decision.
Not DLP, not CASB
DLP is channel-centric. It excels at detecting sensitive content moving through email, cloud apps, and endpoints. CASB and SSE excel at network and SaaS-session posture — which apps are in use, what traffic classes look like, what to block at the edge.
Both matter. Neither was designed for intent-level governance at the prompt boundary. A CASB can tell you someone visited an LLM domain. DLP can flag a file upload. Neither reliably answers: what did this specific interaction contain, what action did policy take, and can you prove it in an audit? AIG fills that gap. It is complementary to DLP and CASB, not a replacement.
The interaction decision
The core primitive in AIG is the interaction decision. For each prompt or tool call, the system evaluates policy and executes one of four actions:
- Allow - proceed unchanged
- Mask - redact sensitive entities before the model or tool sees them
- Block - stop the interaction
- Hold - queue for human review before execution
Each decision produces three linked artifacts: the decision itself, metadata about what was evaluated, and the outcome. That trio is what makes AIG auditable. You are not just logging that "someone used ChatGPT." You are recording what policy decided and why.
What to measure
AIG programs should be measured with governance metrics, not just security ticket volume:
- Interaction policy coverage — what percentage of in-scope AI interactions are evaluated against policy
- Prevented high-risk interactions — count and rate of interactions blocked, held, or masked under high-risk conditions
- Decision-to-evidence latency — time from policy action to auditable record availability
- Cross-surface coverage — governed interactions across browser, MCP connectors, and API ingress
If you cannot measure these, you have visibility, not governance.
Where Blekline sits
Blekline implements AIG as an ingress control plane at Layer 4 of the AI enablement stack, between agent clients (Cursor, Claude Desktop, Copilot) and the models, frameworks, and sandboxes they call.
Three surfaces coordinate around the same policy:
- Browser extension - mask-before-send on supported LLM web surfaces
- MCP server and proxy - govern prompts and tool calls from agent clients
- Web control plane - policy configuration, activity stream, response queues, and compliance exports
The agent does not need to change. The tools do not need to change. Governance is structural, enforced at the call boundary before execution.
Getting started
Most teams fail AI governance by trying to block everything on day one. AIG works better in phases:
- Simulate - run policy against real interaction patterns without blocking; understand what would have been masked or stopped
- Enforce -turn on mask and block for high-confidence rules; keep exceptions narrow
- Audit - export decision-linked evidence for security reviews, SOC 2 programs, and regulatory mapping
Start with one surface, usually the browser extension for shadow AI, or MCP for engineering teams, and expand as coverage metrics justify it.
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