AI is now enterprise infrastructure. It needs enterprise-grade control.
Teams were shipping AI they could not see, test, or prove. In 90 seconds, the film shows that gap and the system we built for it.
Point tools stop at a finding. Zenjin is the trail from what is running, through what was tested, to evidence you can export.
Shadow models, agents, and MCP servers become inventory, not a rumor.
Red teaming and evals write findings into the same trail GRC will export.
Identity, traces, and approvals so you can answer who authorized that.
Registry, trust graph, policy rules, live runs, violations, and approvals, so you can answer who authorized that.
Scan the org, promote what you find into inventory, and produce AIBOM / AgBOM for what actually runs.
OpenAImodel
Postgresmcp
Figmamcp
Notionapp
Slackapp
Hugging Facemodel
Salesforceapp
Mistralmodel
GitHubmcp
Linearapp
Geminimodel
HubSpotapp
Anthropicmodel
Stripemcp
JiraappSimulate real-world attacks against your AI agents to uncover security vulnerabilities, unsafe behaviors, and data exposure risks. Get actionable findings and evidence to strengthen your defenses.
ScoutClaudePlannerOpenAI01 Brief · Orchestrator
Start campaign 14. Probe the support graph.
Goal-based evals from a library, run against catalog models, with regressions feeding the next run and compliance.
Traces, FinOps, experiments, and guardrails. Runtime evidence tied to identity and compliance.
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Discover and classify sensitive data, map how humans and agents reach it, and turn exposure into evidence.
01 Scan · Stores
Six stores in the graph. Slack just connected.
Map Data Security Posture Management, red team, and eval output to the frameworks you run, and keep the packet current.
Zenjin is the control layer for trusted AI. It puts inventory, data posture, testing, identity, and audit-ready evidence in one system.
Detect AI, Data Security Posture Management, agentic red teaming, AI evaluation, agent observability, agentic identity, and compliance.
Scanners and notebooks find a crack or a score. Zenjin keeps those findings on the same record as inventory, identity, and evidence so a board or auditor can follow the trail.
Attacker agents probe your agents, with an orchestrator and a judge, then file what broke into the same evidence trail as Data Security Posture Management and evals.
Request a demo. We start from inventory and the first audit packet, then add Data Security Posture Management, testing, observability, and identity to match how you actually run agents.
