Safeguarding AI Systems at Every Layer

Comprehensive baseline evaluation of AI & ML systems, data pipelines, and APIs to identify vulnerabilities and establish security postures.

Simulate real-world attacks including prompt injection, data poisoning, model inversion, and adversarial examples to stress-test your AI systems.

Strengthen AI models through adversarial training, parameter obfuscation, and input sanitization to resist manipulation and attacks.

Implement intelligent guardrails to intercept malicious prompts, enforce safe output constraints, and prevent harmful content generation.

Secure AI training and inference data through encryption, differential privacy, and federated learning techniques.

Continuous monitoring for model drift, output anomalies, and suspicious inference access patterns in production environments.

Comprehensive security for AI APIs including authentication, rate limiting, sandboxing, and request validation.

Proactive tracking and mitigation of emerging AI-specific threats, vulnerabilities, and attack vectors.

Ensure adherence to EU AI Act, NIST AI Risk Management Framework, and regional AI governance requirements.

Embed security throughout the ML lifecycle with CI/CD integration, sandboxing, and container hardening.

Certify models and validate systems with independent external assessments to provide assurance to stakeholders.

Specialized response services for AI-related breaches including incident handling, rollback procedures, and root cause analysis.

Continuous security operations with round-the-clock monitoring, alerting, and response services for AI systems.

Deploy or build custom internal tools including LLM vulnerability scanners, guardrail modules, and automated testing frameworks.