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Mirawyn

Secure your AI

AI Runtime Monitoring

Ongoing visibility into prompts, model outputs and agent actions, with detections and incident response built for AI.

Why it matters

Many teams have no record of what their models were asked, what they answered or which tools they called. Without that telemetry, an AI incident is hard to detect and harder to investigate.

Mapped to

  • MITRE ATLAS
  • NIST AI RMF
  • NIST CSF 2.0

What we cover

  • Telemetry design for prompts, responses, retrievals and tool calls
  • Detections for prompt injection, data exfiltration and abuse
  • Guardrail tuning based on real traffic
  • Integration with your existing SIEM and alerting
  • Incident response playbooks for AI-specific incidents
  • Regular retesting as models and prompts change

How an engagement runs

  1. 01

    Instrument

    Capture the right signals without collecting more data than you need.

  2. 02

    Detect

    Deploy and tune detections for your applications and agents.

  3. 03

    Respond

    Prepare playbooks and run exercises with your team.

  4. 04

    Improve

    Review trends, retest and adjust controls as your AI evolves.

What you receive

Clear outputs your engineers, leadership and auditors can each use.

  • Logging and telemetry specification
  • Detection rules mapped to AI attack techniques
  • AI incident response playbooks
  • Monthly AI risk and activity reporting

Open-source first

We start with proven open-source tools to keep costs down, and recommend commercial options only when they add real value. How we choose

  • Wazuh
  • Falco
  • OpenTelemetry

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Ready to talk about AI Runtime Monitoring?

Tell us what you are building. We will come back with a short, honest view of where the risk is and what it would take to test it.

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