milgram.dev

Enterprise LLM Firewall

AI Safety. Safe AI.

The security and optimization proxy between your AI tools and their model providers.

Catch agents going off mission.
Give security teams a place to act.

Request beta access OpenAI-Hugging Face replayA Milgram replay surfaced the warning signs.Roughly two weeks of retrospective lead time.

Invite-only beta · For AI platforms and enterprise security teams.

AI traffic protected by Milgram Conceptual policy flow in both directions: every request and response reaches Milgram for inspection. Four examples cycle: clean content passes unchanged; sensitive content passes masked; clean content passes compressed; malicious content is blocked with no outgoing packet. Dots represent masked content and smaller packets represent compression. These are illustrative policy outcomes, not live measurements.
AI toolsmilgramLLM FirewallModel providersInspect every request & responseCleanPrivacy ModeTokens CompressionMalicious Drift / Attack

OPENAI-HUGGING FACE · RETROSPECTIVE REPLAY

AI agents went off mission.

A Milgram replay surfaced
the warning signs.

Task drift. Credential abuse. Privilege escalation.
34 security signals. 12 reconstructed sessions.

The current Milgram engine flagged an evidence-derived reconstruction of the incident. The earliest record maps to activity roughly two weeks before the reported production compromise.

See the evidence Evaluate Milgram

RETROSPECTIVE LEAD TIME

Up to roughly2 weeks

  1. Earliest replay findingPrivilege escalation
  2. Production compromiseReported incident

See the warning signs. Gain time to act.
Bring this visibility to your AI workflows.

Public forensic evidence. Evidence-derived sessions. Flagged by Milgram’s current engine. Explore the replay for source fidelity, reconstruction details, and enforcement scope.

ONE BOUNDARY. THREE OUTCOMES.

Make AI useful.
Keep its behavior accountable.

Centralize the security and cost controls that individual AI tools leave your teams to assemble.

01 / VISIBILITY

See the whole session.

Connect requests, responses, tool activity, and available reasoning. Investigate how an agent’s behavior develops across messages.

Detection & sessions
02 / CONTROL

Protect sensitive context.

Monitor threats, block supported policy violations, and mask sensitive information before it reaches a model provider.

Data protection
03 / EFFICIENCY

Send fewer input tokens.

Reduce redundant history and structural overhead with deterministic compression. Measure savings without adding a summarization-model call.

Inference optimization

DETECTION THAT LEARNS FROM REVIEW

Explainable rules.
AI-assisted evolution.

Deterministic rules, session correlation, and a lightweight neural classifier work together. Through MCP, your chosen AI can investigate findings, correct false positives, and write or adapt detection rules within the permissions you grant.

Reviewed signals feed organization-specific classifier tuning. Large-model review stays outside the live request path.

Explore the feedback loop

BUILT FOR EVALUATION

Understand the fit.
Then prove it on your traffic.

Start with a bounded workflow, establish a baseline, and introduce enforcement as your team validates the results.

Find your workflowSecurity operations, coding agents, and AI platformsPlan your deploymentProvider integration and customer-managed infrastructureBuild your evaluation planCoverage, false positives, enforcement, and cost

INVITE-ONLY BETA

Bring your AI workflow.
Let’s examine the boundary.

Tell us what you use, what you need to protect, and where Milgram would run.

Talk to Milgram