AI Development
AI Security Review
We review the security of LLM/RAG/agent systems: prompt injection, data leakage, tool permissions, logging, and deployment posture. You receive a prioritized report and a clear mitigation plan your team can ship.
Overview
What this service is
An AI security review focuses on risks unique to LLM apps: prompt injection, unsafe tool use, retrieval leakage, and untrusted output paths.
We threat-model your system end-to-end and test real failure modes against your data sources, tools, and policies.
Delivery includes an actionable report, recommended guardrails, and implementation guidance (or we can implement fixes with you).
Start with evidence
Begin with a focused opportunity sprint
In 1–2 weeks, we map one consequential operation, test its automation case, and define a governed implementation plan before a larger build.
Standard
AI delivery standard
Quality and safety practices we ship with AI builds so the system stays measurable, maintainable, and production-ready.
Logging + tracing
Conversation and tool traces with request IDs, error visibility, and debug-friendly runbooks.
Guardrails + safety
Tool allowlists, PII-safe patterns, refusal behavior, and escalation routes for edge cases.
Evals + regression tests
Golden queries, scorecards, and regression checks so quality improves over time instead of drifting.
Cost + latency controls
Caching, prompt discipline, retrieval tuning, and routing so your app stays fast and predictable at scale.
Documentation + handoff
Architecture notes, environment setup, and next-step roadmap so your team can iterate safely after launch.
Security-first integration
Secrets isolation, role-based access, audit-friendly actions, and minimal data retention by design.
Benefits
What you get
Identify prompt injection and data leakage risks
Reduce unsafe tool action surfaces
Improve audit readiness with logs and controls
Clarify privacy and retention posture
Prioritize fixes with clear severity ranking
Ship mitigations with minimal product disruption
Features
What we deliver
Threat model + attack surface mapping
We map data sources, tools, prompts, and user entry points to identify the highest-risk areas.
Prompt injection testing
Adversarial inputs to validate context separation, tool controls, and policy enforcement under real attacks.
RAG leakage checks
Test retrieval filtering, tenant isolation, and permission enforcement to prevent cross-user data exposure.
Tool permission review
Review allowlists, parameter validation, approvals, and RBAC to ensure actions are safe and minimal.
Logging and retention review
Validate what’s logged and stored, ensure PII handling is sane, and recommend safe retention policies.
Actionable remediation plan
Prioritized fixes with practical implementation steps and acceptance criteria.
Process
How we work
Intake
Access, architecture overview, and risk goals.
Threat model
Map data, tools, and trust boundaries.
Testing
Prompt injection, leakage checks, and tool review.
Report
Findings with severity and recommended fixes.
Remediation
Optional implementation support for fixes.
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Security review before enterprise rollout
Validate your AI system’s security posture before enabling broader access and higher-risk tools.
Agent tool access hardening
Reduce the risk of agents taking unsafe actions via strict allowlists and approval patterns.
Permissioned RAG hardening
Confirm retrieval filters and tenant isolation prevent private document leakage.
Incident response improvement
Add logging and monitoring so failures are visible and actionable during incidents.
Compliance-aligned logging and retention
Validate retention and logging behavior to reduce privacy exposure without losing debugging power.
Implementation Patterns
How we frame common AI workflows
Illustrative patterns only—not client case studies, endorsements, or production-result claims.
Regulated mobile data workflow pattern
Challenge: Sensitive data workflows need explicit access boundaries, traceability, and documented operating responsibilities.
Approach: Threat-model the workflow, map authorization rules, select encryption controls, and define auditable state transitions.
Validation: Test access boundaries and recovery paths, record residual risk, and obtain any required independent compliance assessment.
Large knowledge-base retrieval pattern
Challenge: Long, mixed-format source collections need traceable retrieval and safe behavior when evidence is weak.
Approach: Evaluate hybrid retrieval, reranking, citations, structured outputs, and defined fallback or human-review paths.
Validation: Use a representative offline evaluation set and report citation quality, latency, and cost under documented test conditions.
Operations automation pattern
Challenge: Approval and system-sync workflows need deterministic controls around exceptions, retries, and ownership.
Approach: Model the workflow, add validation and approval gates, and use AI only for bounded classification or extraction tasks.
Validation: Baseline manual steps, test exception paths and audit logs, then compare pilot measurements before considering wider rollout.
Explore
Related solutions & technologies
Useful next pages if you’re planning an AI pilot or scaling this into a larger product.
Related solutions
Decision Guides
Not sure which to choose?
FAQ
Frequently asked questions
No. This is an engineering-focused security review for AI systems. For formal audits and certifications, you’ll typically engage a dedicated third-party auditor.
Yes. We test prompt injection and adversarial inputs and validate that policies and tool controls hold under attack-like behavior.
Yes. We review retrieval filtering, tenant isolation, and access enforcement to reduce leakage risks.
Yes. We can provide an implementation plan or directly help ship mitigations with your team.
Architecture docs and code access (or a walkthrough), plus information about your data sources, tools, and deployment environment.
Yes. You get a prioritized findings report with recommended mitigations and acceptance criteria.
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