Secure your AI before customers ask.
AI-native and AI-enabled apps face a threat model that didn't exist five years ago. We help you design and operate the controls customers, investors, and regulators are starting to require.
- Engagement
- Project + ongoing
- Scope
- Models · Agents · APIs
- Frameworks
- OWASP LLM Top 10 · NIST AI RMF
- Output
- Threat model + controls
What's in scope
AI-specific threat model
Specific to your architecture, data flows, and agent capabilities. Not a generic checklist.
Training-data governance
What's used, what's licensed, what's PII, what's logged.
Prompt-injection mitigations
Layered defenses — pre-processing, isolation, output filtering, agent guardrails.
Model exfiltration / inversion controls
Rate limiting, response scrubbing, abuse detection.
Agent-action controls
Tool-use authorization, action audit logs, human-in-the-loop for material actions.
AI customer-trust content
What customers, regulators, and security questionnaires actually ask about.
From threat model to operating controls
- 01Weeks 1–3
Threat model
Architecture review + AI-specific threat modeling.
- 02Months 1–3
Controls implementation
Layered mitigations + agent guardrails + audit logging.
- 03Month 4+
Operating + trust
Monitoring + customer-trust content + ongoing tuning.
What you walk away with
- AI-specific threat model documented
- Training-data governance operating
- Prompt-injection mitigations layered
- Agent-action controls + audit logging
- AI customer-trust narrative + evidence
- Investor-ready AI risk story