Adversarial testing for LLM applications to validate safety controls and data protections in real use conditions.
Let’s Join Us
Class IT 2024
Class Basic
A systematic methodology combining adversarial testing, risk analysis, and remediation to secure AI models and pipelines.
Map prompts, tools, plugins, and data connections exposed to user input.
Run jailbreak, prompt injection, and policy bypass scenarios.
Test moderation, permission boundaries, and output filtering behavior.
Provide control-level fixes for prompts, policy, and architecture.
Key AI risk insights, practical mitigation guidance, and leadership-ready summaries.
This is a structured table that evaluates each API endpoint based on risk.
Strengthen prompts and implement guardrails to prevent misuse and ensure safe AI behavior.
Rank AI risks by business impact to focus mitigation on what matters most.
Provide clear, executive-level insights to support compliance and informed decision-making.
Scoped engagement aligned to your highest-value attack surfaces and risk priorities
Actionable findings with clear remediation ownership across security and engineering teams
Retest-ready closure path to validate fixes and confirm reduced exposure
A focused, risk-aligned engagement targeting critical AI attack surfaces, delivering actionable findings with clear ownership, and ensuring validated remediation through structured retesting to confirm reduced risk exposure.
