Automated AI Risk Testing

Problem Context

QA and security teams test AI agents but struggle because these systems don’t fail like traditional software. They give smooth, confident answers that might still be wrong or dangerous—like leaking customer data in a polite response. Traditional test cases miss these silent failures, leaving teams guessing if their AI is safe.

Pain Points

Teams waste time writing tests that don’t work, while risky flaws slip through. No one owns the problem, and companies can’t prove their AI is secure. Manual reviews slow down development, and undetected flaws risk breaches, lost trust, or legal trouble. Existing tools don’t catch subtle but critical vulnerabilities.

Impact

A single undetected flaw can expose sensitive data, damage reputation, or lead to legal trouble. Teams spend extra hours manually reviewing AI responses, slowing down development. Without proof of safety, companies struggle to get approval for new features. The longer this problem goes unsolved, the more they risk losing customers to competitors with safer AI.

Urgency

AI adoption is growing fast, and regulators are demanding proof of safety. Customers expect reliable AI tools, and companies can’t afford to wait until a major incident happens. Teams need a solution now to avoid falling behind or facing costly mistakes. The risk of silent failures is constant and unpredictable.

Target Audience

Tech companies building AI chatbots or assistants face this. Financial firms using AI for customer service or fraud detection need it. Healthcare organizations relying on AI for diagnostics or patient support can’t ignore it. Any business using AI to interact with customers or handle sensitive data is at risk if they don’t test it properly.

Solution Approach

AI RiskScanner is a lightweight, API-based tool that continuously tests AI agents for silent failures—like data leaks, hallucinations, or biased responses. It uses pre-built attack scenarios (e.g., prompts designed to trigger harmful behavior) and flags risks in real time. Teams get clear, actionable reports without writing custom tests.

Key Features

1. Continuous Monitoring: Scan AI responses daily/weekly and alert teams to new risks.
2. Clear Risk Reports: Flag issues with severity scores and suggest fixes (e.g., 'This prompt triggered a data leak—update your agent’s guardrails').
3. Zero-Code Setup: Connect via API key and start testing in minutes—no code changes needed.

User Experience

QA or security teams add their AI agent’s API endpoint, then AI RiskScanner runs automated tests in the background. They receive daily/weekly reports highlighting risks (e.g., 'Your agent leaked a customer email in this response'). Teams can prioritize fixes based on severity and track progress over time. No manual reviews or guesswork—just clear, actionable insights.

Differentiation

Unlike generic AI testing tools, AI RiskScanner focuses on silent failures—subtle but dangerous issues like data leaks or hallucinations. It uses proprietary attack scenarios (e.g., prompts that trigger harmful behavior) and integrates with popular AI frameworks (e.g., LangChain). Free tools miss these risks, and manual reviews are too slow. Our approach is faster, more accurate, and scalable.

Scalability

Start with 3–4 core attack scenarios, then expand based on customer needs (e.g., industry-specific tests for healthcare or finance). Pricing scales with usage (e.g., per-agent or per-test). As teams grow, they can add more agents or custom scenarios. The API-based model ensures easy adoption across teams and industries.

Expected Impact

Teams reduce manual review time by 80% and catch risks they’d otherwise miss. They get proof of AI safety for compliance and customer trust. Development speeds up because they can approve new features with confidence. The cost of a breach or lost trust is far higher than the tool’s price—making it a no-brainer for AI-driven businesses.