Security for the AI Age.
AI is changing how software is built — and how it's attacked. We secure AI-native applications and use AI to accelerate security operations across your entire stack.
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AI changes the security equation in two ways: it creates new attack surfaces (LLM apps, AI-generated code, ML pipelines) and it enables new defenses (AI-powered triage, automated detection, intelligent code review).
We work on both sides. We secure the AI systems you’re building — LLM applications, ML pipelines, AI-augmented development workflows — and we use AI to accelerate your security operations.
Our AI red team exercises test your LLM integrations against real adversarial techniques, not theoretical checklists. Every finding comes with a remediation plan your engineers can execute immediately.
Engagement Phases
AI Security Assessment
Evaluate AI components against OWASP LLM Top 10. Review AI-generated code pipelines, LLM integration points, data flows, and prompt handling. Identify AI-specific security risks.
Implementation
Implement AI security controls: LLM input/output validation, prompt injection defenses, AI code review automation, AI-powered vulnerability triage, and threat detection tuned for AI attack patterns.
Red Team & Handover
AI red team exercise — adversarial testing of LLM integrations, prompt injection attempts, data extraction probes. Findings report, remediation plan, and team training on AI security practices.
Deliverables
Before & After
| Metric | Before | After |
|---|---|---|
| Vuln Triage Time | Days (manual) | Hours (AI-assisted) |
| AI Risk Coverage | Unassessed | OWASP LLM Top 10 mapped |
| False Positive Rate | High — alert fatigue | Low — AI-filtered, prioritized |
Tools We Use
Frequently Asked Questions
What is the OWASP LLM Top 10?
The OWASP LLM Top 10 is the standard framework for AI application security risks: prompt injection, insecure output handling, training data poisoning, model denial of service, supply chain vulnerabilities, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft. We assess your LLM-powered applications against all 10 categories.
We use AI to write code. Is that a security risk?
It can be. AI-generated code can introduce subtle security vulnerabilities — insecure patterns, deprecated APIs, incorrect access controls — that pass human review because the code looks correct. We configure AI-enhanced SAST rules specifically tuned to catch common AI code generation patterns and integrate them into your PR review process.
What does an AI red team exercise involve?
We adversarially test your LLM integrations: prompt injection to extract system prompts or bypass guardrails, indirect prompt injection via user-controlled data, data extraction probes, model behavior manipulation, and supply chain attacks on your AI tooling. All tests are conducted in your staging environment with your approval.
Do you help secure AI/ML pipelines (not just LLM apps)?
Yes. We secure the full AI/ML lifecycle: training data pipelines, model artifact signing, MLOps platform security, inference infrastructure, and monitoring for model drift and adversarial inputs. This applies to both LLM-based and traditional ML systems.
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