AI Introduces Risks Your AppSec Never Covered
Generative and agentic systems fail in ways traditional application security testing was never designed to catch. Securing them takes a discipline built for the AI attack surface.
New Attack Surface
LLM and agentic applications expose a class of vulnerabilities traditional AppSec never covered: prompt injection, jailbreaks, insecure tool use, and training-data leakage. Standard scanners do not catch them.
Governance Before Incidents
Governance bolted on after a public failure is expensive and reactive. Building audit trails, human-in-the-loop controls, and policy up front turns AI risk into something you can actually defend.
Regulated Data at Stake
When an AI system touches PHI, financial records, or personal data, a single leaked prompt or over-broad retrieval can become a reportable breach. PII exposure analysis catches these paths before customers do.
Customer & Legal Trust
Enterprises now ask for AI security posture in procurement. A written findings report and a documented responsible-AI policy shorten sales cycles and satisfy legal review.
Autonomy Raises the Stakes
Agentic systems that call tools, write to systems, and act on their own reasoning multiply the blast radius of a single bad output. Tool and function-call abuse testing is essential before you grant autonomy.
Shift Left, Ship Faster
Finding an unsafe path in a threat-modeling session costs hours. Finding it in production costs a rollback, an incident review, and reputational damage. Early review is the cheapest security you will buy.
Four Pillars of a Responsible AI Review
Aligned to the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework.
Threat Modeling & Risk Assessment
Map how your AI system can be attacked or misused before it reaches production.
What We Do
Outcomes
- Documented risks ranked by impact
- Clear ownership per mitigation
- Security baked into architecture
- No surprises at launch review
Adversarial Testing
Actively probe your model and prompts the way a real attacker would.
What We Do
Outcomes
- Reproducible exploit findings
- Hardened prompts & guardrails
- Validated input/output filtering
- Fewer paths to unsafe output
Data Privacy & PII Protection
Ensure sensitive and regulated data never leaks through your AI surface.
What We Do
Outcomes
- Mapped PII flows end-to-end
- Reduced data-leak surface area
- Privacy controls at input & output
- Regulator-ready data handling
Governance & Auditability
Prove what your AI did, when, and why, with controls your legal team can stand behind.
What We Do
Outcomes
- Every AI decision traceable
- Evidence ready for auditors
- Clear escalation & override paths
- Governance framework in writing
Risks We Test For
Our review maps to the OWASP Top 10 for LLM Applications, the industry reference for AI-specific vulnerabilities.
Deliverables
A real report your engineering, legal, and security teams can act on, not a sales deck.
- AI risk and threat model assessment
- Prompt injection and jailbreak vulnerability review
- Data privacy and PII exposure analysis
- Model governance and audit trail recommendations
- Written security findings report with prioritized fixes