Cybersecurity for AI companies.
Model providers, agentic-AI startups, and AI-native apps face a threat model that didn't exist five years ago. We help with the controls, governance, and audit posture investors and enterprise customers expect.
- Sector
- AI-native · Foundation · Agentic
- Top risk
- Training data + agent action
- Customer ask
- AI security questionnaires
- Engagement
- Threat model + controls
Threats we routinely see in this sector
Training-data exfiltration
PII or proprietary data leaking through training, fine-tuning, or RAG indices.
Prompt-injection chains
Indirect injection via documents, websites, or tools enabling agent abuse.
Model exfiltration / inversion
Adversaries extracting model weights or reconstructing training data via API.
Agent-action abuse
Agents granted tool access being manipulated into unauthorized actions.
Vendor / model-provider risk
Concentration risk in foundation-model providers and inference vendors.
How we typically engage
- 01Start
AI threat model
Specific to your architecture, data flows, and agent capabilities.
- 02Quarter 1
Controls implementation
Data governance, prompt-injection mitigations, agent guardrails.
- 03Quarter 2+
Audit posture + ongoing
SOC 2 + AI-specific controls + MDR for agent telemetry.
What clients in this sector walk away with
- AI-specific threat model documented
- Training-data governance operating
- Prompt-injection mitigations layered
- Agent-action controls + audit logging
- Customer-facing AI security trust content
- Investor-ready AI risk narrative