Generative AI Security by Design
Secure production GenAI as a system, not a prompt. We design enforced data boundaries, permissioned retrieval, governed tool access, runtime guardrails, and forensic observability - so copilots and agents can operate safely in real workflows without leakage or unauthorized actions.
- Prevention first: boundaries, permissions, and contracts
- Covers RAG and agentic tool use (not only chat UIs)
- Designed for regulated environments and audit-friendly evidence
The Challenge
GenAI systems are attacked through predictable surfaces: prompt injection, retrieval poisoning, unclear data boundaries, over-permissioned tools, and weak observability that makes incidents hard to explain.
Many teams rely on prompt rules and UI guardrails while the real risk sits deeper - in retrieval, tool access, identity, and the absence of enforceable contracts.
Our Approach
We design security into the architecture: data boundaries, permissioned retrieval, tool registry/contracts, identity-scoped execution, and runtime guardrails that are enforced - not implied.
The result is a blueprint your teams and vendors can implement with clear risk boundaries, measurable controls, and evidence that stands up to internal assurance and external scrutiny.
Key Outcomes
Core Deliverables
Threat Model and Risk Surfaces
A practical GenAI threat model focused on your system: data flows, retrieval, tools, identities, and operational touchpoints - mapped to mitigations you can enforce.
- Attack paths: injection, leakage, retrieval poisoning, unsafe tool actions
- Risk boundaries: what is permitted, what is blocked, what requires approval
Global Energy Provider
The Context
Designed a secure RAG architecture for sensitive engineering data, with permissioned retrieval and strict data boundaries.
The Outcome
Result: Deployed trusted internal Q&A assistant to 5,000+ engineers with verified citations and zero leakage posture.