01
Direct Access
You talk directly to engineers who inspect the system, name the tradeoffs, and do the work.
What We Solve
AI leakage rarely comes from one bug. It comes from too many weak boundaries in sequence.
We look at how sensitive data enters the system, what can retrieve it, how long it lives, what agents can do with it, and how it can escape through logs, outputs, or tool calls. This matters when teams are moving fast with RAG, copilots, internal assistants, or agent workflows but have not yet hardened the real data paths.
That usually shows up as over-broad retrieval that returns more context than the user or agent should see, cross-tenant exposure in indexes, caches, or memory layers, tool-using agents that can exfiltrate data through actions or connectors, and weak role boundaries between user permissions and AI permissions.
What You Get
- Data-flow map showing where sensitive content enters, moves, persists, and exits
- Leakage risk register prioritized by impact, likelihood, and exploit path
- Boundary design for retrieval scope, tenant isolation, and permission checks
- Output protection strategy including redaction, escalation, and review points
- Operational recommendations for memory, logs, analytics, and vendor exposure
- Evidence pack security, engineering, and procurement can all use
Controls and Delivery
Data Boundary Design
- Classification-aware routing for prompts, retrieval, memory, and outputs
- Least-privilege context assembly for user and agent workflows
- Tenant separation rules for indexes, caches, histories, and shared services
- Secrets handling and token scope review across tool-connected AI flows
Leakage Prevention Controls
- Retrieval filters, scoped indexes, and pre-output checks
- Redaction patterns and controlled disclosure logic
- Approval gates for sensitive actions or high-risk responses
- Log and trace hygiene to avoid creating new leak surfaces
Validation
- Scenario-based leakage tests across user roles and tenant boundaries
- Prompt and workflow abuse cases for internal and external actors
- Verification of runtime controls under realistic usage patterns
- Retest loops after guardrails, routing, or policy changes
Typical Outcomes
- Cleaner AI architecture for sensitive data environments
- Better answers to customer security questionnaires
- Lower internal risk from support, knowledge, and agent tools
- A workable path to scale AI without constant leakage anxiety
Why Teams Choose SToFU Systems
Senior-led delivery. Clear scope. Direct technical communication.
02
Bounded First Step
Most engagements start with a review, audit, prototype, or focused build instead of a giant retained scope.
03
Evidence First
Leave with clearer scope, sharper priorities, and a next move the business can defend under scrutiny.
Delivery
Senior-led
Direct technical communication
Coverage
AI, systems, security
One team across the stack
Markets
Europe, US, Singapore
Clients across key engineering hubs
Personal data
Privacy-disciplined
GDPR, UK GDPR, CCPA/CPRA, PIPEDA, DPA/SCC-aware