// services
AI Security Reviews
AI systems create new attack surface. We check it before someone else does.
In shortTriton Foundry runs security reviews on AI deployments — prompt injection, data leakage, permission bleed, model and vendor risk — and uses AI-accelerated tooling to deepen traditional software security checks. Findings arrive ranked with fixes, and the same team can implement the hardening.
Why do AI deployments need their own security review?
Because the failure modes are new. A classic web app fails through injected code; an AI system fails through injected language — a poisoned document that rewrites the assistant’s instructions, a retrieval index that ignores permission boundaries, an integration that lets a chatbot act with more authority than the person prompting it. These weaknesses pass traditional scans cleanly. Reviewing them requires understanding both the security discipline and how these systems are actually built — and Foundry builds them.
What gets tested?
The whole chain, not just the model. Where data enters: connectors, uploads, mail, and the permissions they inherit. What the system is told: system prompts, tool definitions, and how untrusted content mixes with instructions. What comes back: output handling, action authority, and logging. Around it all: tenant configuration, secrets, dependency versions, and the vendor agreements deciding where your data sleeps. The review produces a finding register ranked by exploitability and impact, written so both an engineer and an owner can act on it.
What is an AI-accelerated security check?
The same rigor pointed at conventional software, with AI multiplying coverage. Automated review sweeps code, configurations, and dependency trees at a depth manual audits rarely afford, and engineers verify every finding before it reaches the report — no raw scanner dumps, no false-positive homework left to you. It suits pre-purchase software due diligence, periodic health checks on line-of-business systems, and post-incident hardening.
What happens after the report?
Your choice, with no lock-in: your team fixes from the register, or Foundry implements the hardening directly — the same engineers who found the issues, backed by a parent company that has secured business infrastructure since 2001. Re-testing after remediation is included in the engagement, so “fixed” is a verified state, not a hopeful one.
// common questions
AI Security Reviews: common questions
What is unique about securing AI systems?
The attack surface moves into language. Prompt injection turns documents and emails into instructions. Retrieval systems can leak content across permission boundaries. Assistants inherit every over-shared file in the tenant. Traditional controls do not see these paths, so a dedicated review discipline is required.
What does an AI security review cover?
Architecture and data-flow mapping, permission and tenant configuration, prompt injection and jailbreak resistance, retrieval boundary testing, output handling, logging and auditability, vendor terms and data residency, and dependency risk in the AI supply chain. Findings arrive ranked by exploitability and business impact, each with a concrete fix.
Can you check software we're buying or already run, not just AI?
Yes. AI-accelerated review lets us audit codebases, configurations, and integrations faster and deeper than manual-only assessments — dependency risk, secret handling, access design, exposed surfaces. The deliverable is the same: ranked findings, concrete fixes, and optional remediation by the same engineers.
Is this a compliance audit?
It is the technical half. Security review establishes what is actually true about your systems; the compliance practice maps those truths to frameworks like HIPAA, CMMC, and state privacy law. Many clients run both together — see AI Compliance Checks.
// next step
Have a system in mind?
Describe what you are trying to build or fix. A senior engineer reviews every inquiry and responds directly, with a technical read on the problem.