Built by
Nicholas Vidal
AI Security Architect · 20 years in federal cyber defense, incident response & RMF compliance · Now building security programs for AI companies
AI Security Operations: From Documented Controls to Continuous Enforcement
Most frameworks tell you what controls to have. AI SecureOps ensures they're actually working — with real-time verification, automated evidence collection, and audit-ready documentation.
The Challenge
Security chaos kills AI companies
Most AI startups face the same security failures. These problems compound until they block deals, delay funding, or cause breaches.
No security charter or documented risk ownership
Ad-hoc controls that won't survive audit
AI models shipping without threat models
Vendor chaos — no visibility into third-party risk
Engineering blocked by security questions
VCs asking about SOC 2 timeline
The Solution
One framework. Complete coverage.
AI SecureOps Framework™ is a structured, repeatable system that addresses company security and AI-specific risks in parallel.
SOC 2 Type I in 12 weeks
Clean audit path with documented controls and evidence
AI Security Baked In
Threat models, prompt security, and model governance
vCISO Ready
Documented foundation that any security leader can inherit
Engineering Velocity
Security guardrails that accelerate, not block
Framework Structure
Two lanes. One execution layer. Complete coverage.
Security for your company and security for your AI product run in parallel, with SecureOps providing continuous enforcement across both.
Lane 1
Company Security
- Identity & Access Management
- Cloud Security Posture
- Logging & Monitoring
- Incident Response
- Vendor Risk Management
- Evidence Collection
Lane 2
Product & AI Security
- Secure SDLC Integration
- Threat Modeling Templates
- AI Data & Model Risk
- Prompt Injection Mitigation
- Secure Defaults
- Customer Data Boundaries
Execution Layer
SecureOps
Continuous Enforcement
Controls verified in real-time, not just during audits
Real-time Monitoring
AI runtime signals, identity events, data movement
Automated Evidence
Audit-ready documentation generated continuously
Trust
Trusted by AI companies from Seed to Series C
Building security programs that satisfy investors, win enterprise deals, and scale with the business.
Case study coming soon
Case study coming soon
Case study coming soon
Credibility
Built by someone who understands the stakes
This framework is designed by a practitioner with direct experience building security programs that survive audits, satisfy enterprise customers, and enable engineering velocity.
- SOC 2 Type II audit experience
- NIST AI RMF implementation
- Enterprise security architecture
- Startup security programs (Seed to Series C)
- AI/ML product security
"Security should be a competitive advantage, not a blocker. The best security programs enable engineering teams to move faster with confidence."
Nicholas Vidal
AI Security Architect
Ready to build a security program that scales?
Start with a discovery call to assess your current state and define a path to certification.