Senior Engineers, Not Layers of Mediation
Direct access to engineers who can inspect, decide, and execute.
What We Solve
Protect AI systems where output quality is only part of the problem.
AI governance is less about slide decks and more about whether the system has enforceable boundaries: who can authorize it, what it can access, what it can execute, and how teams can prove control after something goes wrong.
We focus on the operational risk layer around AI: injection, over-permissioned agents, data exposure, cross-tenant mistakes, weak review flows, and the absence of logs that make incidents impossible to explain.
- Prompt injection and unsafe instruction chaining
- Over-broad agent permissions across tools and systems
- Sensitive data leakage in retrieval, prompts, or outputs
- Weak tenant boundaries in shared AI workflows
- No audit trail for actions, prompts, or decisions
- Policy gaps around human approval and escalation
- Vendor and model risk without clear control points
- Enterprise friction when AI features cannot survive security review
If you cannot govern the AI system, you do not really control it.
What You Get
- Threat model for prompts, tools, data, and runtime actions
- Authorization design for agents, users, tools, and escalation paths
- Guardrail architecture for sensitive actions and high-risk flows
- Logging and review model for auditability and incident response
- Governance package leadership, engineering, and security can all use
Controls and Delivery
Security Layers
- Prompt, retrieval, tool, and output-path analysis
- Identity and authorization boundaries for AI agents
- Data scoping, tenancy rules, and secrets handling
- Human approval flows for sensitive operations
Validation
- Scenario-based testing and AI red-team cases
- Runtime logging and explainability requirements
- Policy checks for production release readiness
- Remediation sequencing and control validation
Typical Outputs
- AI control map and risk register
- Authorization and approval design
- Guardrail recommendations and rollout logic
- Evidence pack for procurement or internal review
Business Fit
- AI products heading into enterprise sales
- Internal agents touching live systems or sensitive data
- Teams under compliance, legal, or procurement pressure
- Organizations that want AI capability without avoidable trust failures
Why Teams Choose SToFU When Stakes Are High
Senior engineering. Clear decisions. Real outcomes.
Commercially Useful Outputs
Scope, priorities, remediation, and next steps your team can use immediately.
Built for AI-Era and High-Stakes Systems
AI-native platforms, native software, secure systems, and low-latency infrastructure.
Share the system, the pressure, and the deadline. We will turn that into a concrete next move.