Healthcare Law Enforcement Enterprise
For Law Enforcement & Public Safety

Security testing for mission-critical AI that can't tolerate downtime, data exposure, or unverified deployment.

Dispatch, records, and investigative AI systems don't get a second chance at a failure. RedLens tests for containment escapes and adversarial attacks before deployment, runs on your own infrastructure when your environment requires it, maps every finding to the NIST AI RMF, and cites FBI CJIS Security Policy controls in the evidence package as audit evidence, not as a certification.

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Download Solution Sheet (PDF)
ON-PREM RMF SLA ACE
On-Premise Deployment
Runs on your infrastructure, with your data staying there, on Enterprise

For agencies with data-residency requirements, a self-hosted Docker deployment is available on the Enterprise tier: prompts, results, findings, users and reports stay in your own database on your hardware, and RedLens has no access to the deployment. It is on-premise, not air-gapped. Attack generation and judging call a third-party LLM provider's API using your own key; every other outbound destination can be firewalled off.

  • Self-hosted Docker deployment; your data stays in your own database
  • One outbound dependency: a third-party LLM provider's API, with your own key
  • Available on Enterprise, starting at $150k/yr
DATA STAYS ON YOUR HARDWARE
Containment-Escape Detection
Verify your AI agent can't break out of its sandbox before it's deployed

A dispatch or records AI that breaks containment can take unauthorized actions or expose data outside its intended scope. RedLens tests specifically for this: network escape, behavioral deviation, and micro-step attack chains an agent could use to step outside its boundary.

  • Tests for AI containment / sandbox escape specifically
  • Network escape and behavioral-deviation detection
  • Part of RedLens's Master Attack Schema v2.0
ESCAPE ATTEMPT DETECTED
Digital Twin Synthetic Sandbox
Test dispatch and records-adjacent AI without touching a real case file

RedLens generates synthetic, clearly-labeled dispatch and records-style data for testing: mock incident logs, unit IDs, and case identifiers, none derived from real calls or records. Your dispatch-assistance or records-query AI gets tested under realistic adversarial conditions without any real, sensitive data entering the assessment.

  • Synthetic dispatch logs and case-style records per test run
  • Nothing derived from real calls, records, or CAD data
  • Safe to run against dispatch, triage, and records-query agents
NIST AI RMF and CJIS control mapping

Mapped to real frameworks, not a certification we don't hold

Every RedLens finding maps to the NIST AI Risk Management Framework. The evidence package also cites the FBI CJIS Security Policy control by control (CJISSECPOL v6.1, cross-referenced to v5.9.5), with every control id read from the policy PDFs themselves and each one marked evidence recorded, partial, or gap. Enterprise deployments can add a custom SLA. We're direct about what that does and doesn't mean for agencies with formal certification requirements. Read the CJIS Security Policy and AI guide →

The FBI does not certify products or vendors as CJIS compliant, and RedLens holds no CJIS authorization, FedRAMP authorization, or any other government certification. What's available today: NIST AI RMF-mapped findings, CJIS Security Policy control citations in the evidence package, on-premise Docker deployment, and a custom SLA on Enterprise. Your CJIS Systems Officer or CJIS Systems Agency remains the authority on your agency's compliance. If your deployment requires FedRAMP authorization, evaluate accordingly and talk to us directly about your specific requirements.
Why It Matters Now

AI-enabled attacks are already outpacing legacy security tools

Mission-critical public safety systems are exactly the kind of high-consequence environment general-purpose AI security tools weren't built to test.

1 in 4
Malicious breaches are now AI-enabled, a share that grew 56% year-over-year, and they cost organizations $6M on average. Public safety systems that fail don't just cost money. They cost trust and, potentially, safety.
20%+
Of organizations reported a breach that specifically targeted their AI models or applications. The most common ways in were compromised APIs, plugins, and cloud misconfigurations. Dispatch and records systems increasingly run through exactly those integration points.
Source: IBM 2026 Cost of a Data Breach Report, with the Ponemon Institute, published July 29, 2026.
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Find out if your dispatch or records AI can be broken out of before someone else finds out first

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