Ensure AI Resilience Against Real-World Threats

AI Security Assurance tests your AI against real-world threats to ensure resilience and trust

AI Security Assurance tests your AI against real-world threats to ensure resilience and trust

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AI/ML systems are not immune to traditional and novel cyber threats, from adversarial inputs to model theft. With our AI Security Assurance services, we go beyond reviews to simulate real-world attacks, identify hidden vulnerabilities, and help you fortify your AI/LLM deployments. Our ethical hacking, red teaming, and code review services ensure your AI models perform reliably and securely, even under malicious pressure.

Capabilities

AI/LLM
Penetration Testing

Emulate real-world exploits to identify weaknesses in model architecture, APIs, training pipelines, and access control layers.

Red Team
Assessments

Use MITRE ATLAS-based adversarial simulation to test AI/ML resilience, including attacks like model inversion, extraction, and poisoning.

Model
Code Review

Analyze AI model codebases, both manually and using automated tools, to identify security vulnerabilities, privacy risks, and logical flaws.

Vulnerability
Management

Detect and triage AI-specific vulnerabilities (e.g., inference manipulation, insecure plugin design) with continuous monitoring and remediation workflows.

Bias and Ethical
Risk Detection

Detect and mitigate biased outcomes through fairness metrics and establish a governance framework to ensure ongoing ethical AI performance.

AI/LLM
Penetration Testing

Emulate real-world exploits to identify weaknesses in model architecture, APIs, training pipelines, and access control layers.

Red Team
Assessments

Use MITRE ATLAS-based adversarial simulation to test AI/ML resilience, including attacks like model inversion, extraction, and poisoning.

Model
Code Review

Analyze AI model codebases, both manually and using automated tools, to identify security vulnerabilities, privacy risks, and logical flaws.

Vulnerability
Management

Detect and triage AI-specific vulnerabilities (e.g., inference manipulation, insecure plugin design) with continuous monitoring and remediation workflows.

Bias and Ethical
Risk Detection

Detect and mitigate biased outcomes through fairness metrics and establish a governance framework to ensure ongoing ethical AI performance.

Use Cases

Safe Deployment of AI

Vulnerabilities in AI code compromise security. Our code review identifies risks, ensures best practices, and enhances performance before deployment.

AI Risk Assessment

AI systems may hide critical vulnerabilities. Our targeted testing uncovers risks in models, APIs, and infrastructure before attackers do.

Simulation and Resilience Testing

Real-world attacks require real-world defense. We simulate adversarial scenarios to expose weaknesses and improve system resilience.

AI Ethics and Bias Evaluation

Bias in AI models can lead to unfair outcomes. We assess and mitigate ethical risks to ensure that AI systems are fair, transparent, and trustworthy.

AI Vulnerability Management for Lifecycle Security

AI systems need ongoing security oversight. Our solution identifies, tracks, and mitigates vulnerabilities across the AI lifecycle.