Shipping AI fast doesn’t mean shipping it safely
Enterprises are deploying GenAI at speed, but traditional security assessment wasn’t built for AI-specific risks. Teams face a consistent set of blind spots.
Unknown exposure
No clear answer to “Is our AI app safe to ship?” Manual testing is inconsistent and doesn’t scale across teams or releases.
Prompt injection & jailbreaks
Users can override system instructions, extract hidden data, or bypass safeguards and you won’t know until it’s exploited.
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Data leakage & insider threat
Sensitive data, proprietary logic, or internal information can be extracted from public-facing models or exploited by insiders through crafted prompts.
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No audit-ready proof
Missing evidence and reporting for internal risk reviews, compliance expectations, and security sign-off before launch.
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Controlled Adversarial testing, end to end
AI Red Teaming runs structured attack campaigns against your AI systems—chatbots, RAG apps, agent workflows, LLM APIs—captures evidence of every failure, and turns results into actionable findings with remediation guidance.
Dynamic runtime security assessment for your AI
Target onboarding & scope control
Test different AI surfaces while maintaining enterprise boundaries. Supports internal chat apps, RAG assistants, agents with tool access, LLM APIs, gateways, and homegrown apps. Includes environment scoping, rate limiting, and safety controls.
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Automated red teaming campaigns
Run structured test campaigns on demand or on a continuous schedule. Configure concurrency limits, stop-on-critical rules, and safe execution settings to ensure repeatability across releases.
Privacy-first architecture
Built-in 5000+ test packs aligned to common AI attack patterns:
Prompt injection and jailbreaks,
Policy bypass and instruction override, Data exfiltration and sensitive information leakage, Social engineering and manipulation scenarios Unsafe content elicitation, Agent and tool manipulation
Custom test builder
Create internal tests using prompt templates, variables, and multi-turn flows. Define pass/fail criteria and expected safe behavior. Tag tests by team, severity, and reuse.
Live results & risk analytics
No passwords, no new tools to learn, no behavior change needed. Install and protect immediately.
Findings, evidence & triage workflow
All processing happens locally in the browser. Data is analyzed in memory and immediately discarded. Nothing stored unless you enable audit logging.
Reporting & audit readiness
Generate executive summaries and technical appendices. Export PDF, CSV, or JSON for launch approvals, security reviews, compliance, and third-party risk assessments.
Dynamic runtime security assessment for your AI
Repeatable, not manual
Structured campaigns that run consistently across teams and releases—not ad-hoc prompt testing in a chat window.
Evidence-first workflow
Every failure produces traceable evidence with severity, remediation, and ownership—ready for security reviews.
Unified governance
Operates inside the Nyuway management plane with tenant isolation, RBAC, centralized audit logs, and consistent controls.
What teams achieve
- Clear visibility into AI risk before release, with repeatable tests and evidence
- Measurable reduction in jailbreak and data leakage risk through re-testing after fixes
- Safer agent and tool enablement by validating agents can’t be tricked into unsafe actions
- Faster security reviews and smoother launch approvals through standardized validation
- Audit-ready proof of ongoing AI security testing via exportable reports and evidence trails
