AI-Security
AI features open an attack surface classic AppSec doesn't cover: untrusted text becomes control flow, retrieval pulls in data you never vetted, and agents act with your credentials. We help you ship RAG and LLM systems that hold up under adversarial use — and prove it.
Where AI systems break
The failure modes are architectural, not provider-specific — which is why a model swap doesn't make them go away.
Prompt injection
Direct and indirect instructions — hidden in retrieved or poisoned content — hijack the model’s behavior.
Data exfiltration & leakage
System-prompt leakage, secret disclosure, cross-tenant bleed, capture via logs or training data.
Retrieval poisoning (RAG)
Untrusted documents in the index steer answers or inject instructions; weak tenant isolation of the vector store.
Tool & agent abuse
Over-privileged tools, unconfirmed side-effects, unsandboxed code execution acting on the user’s behalf.
Broken AuthZ for AI
The model reads or acts beyond what the user is allowed to — authorization that never reached the AI layer.
No safety net
No adversarial evaluations, so regressions — a new jailbreak, a leaky prompt change — ship silently.
Engagement packages
Fixed-scope, so you know what you get. Start with an assessment, or go straight to design and implementation.
How an engagement runs
From "where do we stand" to a safety net your team owns.
- Assessment A documented threat model of your RAG/agent architecture and a prioritized findings report.
- Architecture Guardrail and isolation design, tied to your existing authorization and audit — jSentinel fits here naturally.
- Hands-on Secure-by-default patterns and an adversarial evaluation harness your team owns and runs in CI.
Who it's for
Teams adding RAG, chat, copilots or agents to a product — especially in regulated or multi-tenant settings, where a data leak or an over-eager agent is a real incident, not a theoretical one.
On the JVM already? Authorization for AI maps cleanly onto the same decision model — the jSentinel integration is direct.
Questions, answered
Do you need access to our models and prompts?
For an assessment, an architecture overview is enough to start. Deeper work benefits from access to prompts, retrieval config and tool definitions.
Which stacks and models do you support?
Model- and vendor-agnostic — the failure modes are architectural, not provider-specific, so the approach holds across stacks.
How does this relate to jSentinel for Java?
Authorization for AI maps cleanly onto the same decision model. If you’re on the JVM, the integration is direct. See jSentinel for Java →
We want to build the AI system, not just secure it.
That’s jSentinel AI — we build AI apps, corporate LLMs, RAG systems and assistants, with this security expertise baked in by default.