Automated AI Red Teaming Services

Automated AI red teaming uses software-driven adversarial simulation to continuously probe AI models, LLMs, and agentic systems for exploitable vulnerabilities. Unlike manual testing, it operates at scale and speed—running thousands of attack scenarios including prompt injections, jailbreaks, and data poisoning attempts—without requiring constant human intervention, ensuring comprehensive and repeatable coverage.

Traditional penetration testing targets network, application, and infrastructure vulnerabilities. AI red teaming specifically targets the unique attack surfaces of machine learning models and LLMs—such as adversarial inputs, model inversion, training data extraction, and prompt manipulation. It requires specialized AI security expertise and tooling that goes well beyond conventional pen testing methodologies.

Protectt.ai's platform supports red teaming for large language models (LLMs), generative AI applications, ML classification and prediction models, agentic AI pipelines, and AI-integrated APIs. It covers systems in development, staging, and live production environments, making it suitable for enterprises deploying AI across banking, fintech, insurance, and enterprise platforms.

Our platform simulates a broad range of AI-specific attacks including prompt injection, jailbreak attempts, goal hijacking, adversarial input crafting, model inversion attacks, training data extraction, backdoor trigger probing, and supply chain integrity attacks on ML models. Attack scenarios are continuously updated to reflect the latest threat intelligence and emerging adversarial techniques.

Protectt.ai is certified to ISO 42001 (AI Management Systems), ISO 27001 (Information Security), ISO 22301 (Business Continuity), and PCI DSS. Our red teaming services are designed to align findings and remediation guidance with these frameworks, helping enterprises demonstrate audit-ready compliance and meet regulatory requirements across financial services, fintech, and government sectors.

A focused automated AI red teaming assessment can deliver initial findings within days, depending on scope and system complexity. Continuous monitoring and re-testing is an ongoing service. For enterprises requiring full agentic AI lifecycle protection, we work with your team to define phased engagement timelines—typically spanning initial assessment, hardening support, and continuous coverage phases over several weeks.

Protectt.ai's automated red teaming is designed with zero performance overhead as a core principle. Testing can be conducted in isolated sandbox environments or against non-production replicas to eliminate risk to live systems. Where production testing is required, our controlled testing methodology ensures minimal operational impact while maintaining thorough adversarial coverage.

You receive a comprehensive red team report detailing all discovered vulnerabilities, their risk severity scores, proof-of-concept attack evidence, and prioritized remediation playbooks. Reports are designed to be actionable for both security and engineering teams. For continuous monitoring engagements, you also gain access to a real-time dashboard tracking your AI system's ongoing security posture.