SOLUTIONS
A shared boundary for teams building with AI.
Give security, engineering, and platform teams a common view of AI behavior, sensitive context, and inference overhead.
START WITH THE WORKFLOW
The same traffic boundary.
Different operational priorities.
Milgram is designed for organizations adopting AI tools and agents across engineering and business workflows. An effective rollout starts with one accountable team and a clear security or cost question.
Investigate behavior
with its context.
For incident responders and detection engineers who need to understand what an AI system was doing when a signal appeared.
The problem
A credential read, unusual instruction, or out-of-scope action is hard to assess without the surrounding task. Fragmented application logs force analysts to reconstruct the sequence.
The workflow
Inspect the detection, matched rule, relevant messages, and session history in Milgram. Distinguish a real policy violation from authorized work. Use a customer-controlled AI through MCP to help assess evidence, record corrections, and draft improved rules.
What to measure
Evidence completeness, time to reach a supported assessment, false-positive burden, and whether another analyst can reproduce the reasoning. Correlate findings with infrastructure telemetry when the event crosses the AI traffic boundary.
Explore AI-assisted investigationKeep useful automation
inside its intended scope.
For engineering leaders and security teams adopting agents with access to repositories, logs, or operational tools.
The problem
Tool access makes an agent productive, but it also brings sensitive data and consequential permissions into its workflow. A model’s requested action can extend beyond the original task.
The workflow
Route supported AI traffic through Milgram and begin in shadow mode. Review task drift, credential exposure, and suspicious tool-related content. Add validated data-masking or blocking policies while retaining repository permissions, sandboxing, and tool authorization.
What to measure
Coverage of actual tool calls and results, detection on authorized and unauthorized examples, the timing of enforcement, and successful completion of normal development work.
Review the incident replayCentralize controls.
Understand inference cost.
For platform owners supporting multiple AI applications, teams, and provider integrations.
The problem
Teams otherwise implement data handling, request logging, and prompt optimization separately. That makes policy consistency and operational troubleshooting harder.
The workflow
Integrate a representative provider path, connect its traffic to sessions, and define policy ownership. Introduce deterministic compression where repeated context is significant. Evaluate a deployment topology that matches your data-handling and operations requirements.
What to measure
Client compatibility, streaming behavior, failure handling, access boundaries, input-token savings, and task correctness. Establish monitoring and a tested rollback path before onboarding additional teams.
Plan deploymentChoose a first evaluation.
| Your first question | A useful starting point | Read next |
|---|---|---|
| What is our AI sending? | One assistant workflow with representative prompts and tool outputs. | Data protection → |
| Can we detect agent drift? | Known benign and out-of-scope trajectories in shadow mode. | Detection layers → |
| Can we reduce inference spend? | A repeatable long-context workload with task-quality checks. | Compression → |
INVITE-ONLY BETA
Bring your workflow. Define your evaluation.
Tell us what you use, what you need to protect, and where Milgram would run.