01
AI red-teaming
Structured adversarial testing of your models and agents against jailbreaks, injection and misuse.
AI Cybersecurity
Security for the model layer, not just the network.
AI systems introduce new ways to fail: prompt injection, data leakage, poisoned inputs, over-permissioned agents. We test for them, defend against them and monitor for them continuously.
- CoverageModels, agents, data, APIs
- TestingAdversarial red-teaming
- MonitoringContinuous, not periodic
- Aligned withNIST AI RMF, OWASP LLM Top 10
What we do
The attacks traditional security wasn’t built for.
Firewalls and endpoint tools don’t see a malicious instruction hidden in a PDF or a model quietly leaking training data. We do.
02
Prompt-injection defense
Input isolation, tool permissioning and output checks that stop hidden instructions from taking control.
03
Data leakage prevention
Controls that stop models and agents from exposing sensitive data in responses or logs.
04
Agent permissioning
Least-privilege tool access, approval steps for risky actions and complete action logs.
05
Threat monitoring
Detection of abuse patterns, anomalous usage and drift in model behavior in production.
06
Compliance mapping
Evidence and documentation mapped to NIST AI RMF and your existing security program.
How it works
Four steps, one accountable team.
01
Threat model
We map your AI systems, data flows and the ways each could be abused.
02
Red-team
Hands-on adversarial testing with a prioritized findings report.
03
Harden
We implement or guide the fixes and verify that they hold.
04
Monitor
Ongoing detection, alerting and periodic re-testing as your systems change.
What you get
Delivered, documented, operated.
- Threat model of your AI systems
- Red-team report with prioritized findings
- Implemented guardrails and permission controls
- Monitoring and alerting in production
- Re-testing after significant changes
- Documentation for auditors and customers
Works with
OWASP LLM Top 10MITRE ATLASNIST AI RMFSIEM integrationSSO / RBACAudit logging
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