TestSavant provides an assurance stack for continuously red-teaming, defending, and certifying agentic workflows. The platform unifies attack libraries, adaptive guardrails, and evidence-grade reporting to accelerate AI deployment velocity while managing risk. It enables product, security, and GRC leaders to embed automated assurance into the development lifecycle for production readiness.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises deploying generative AI applications face increasing risks from vulnerabilities such as data poisoning, prompt injection, and toxic outputs, which can lead to costly breaches, non-compliance, and reputational damage. Existing AI guardrails often fail to provide comprehensive security, leaving organizations exposed to evolving threats in complex AI environments.
Solution
TestSavant provides an autonomous security platform that integrates with enterprise AI applications to protect against risks such as data poisoning and prompt injection. The platform offers real-time monitoring and adaptive guardrails, ensuring compliance and safeguarding data integrity in complex AI environments. TestSavant's platform integrates algorithmic red teaming, a threat intelligence pipeline, and compliance frameworks to generate input examples that reveal weaknesses in models and applications. The platform offers code-level protection, multimodal guardrails, and AI-augmented reports to secure generative AI systems. TestSavant's adaptive security solutions defend against risks like data poisoning, prompt injection, and toxic outputs, empowering organizations to secure AI systems and stay ahead of evolving threats.
Target Audience
TestSavant's primary customers are enterprises adopting Generative AI who need adaptive, security-first solutions to protect their systems, maintain data integrity, and meet compliance requirements.
Features
- Real-time monitoring of AI interactions and outputs
- Adaptive guardrails that learn from incidents and adjust security protocols
- Algorithmic red teaming to identify vulnerabilities
- Threat intelligence pipeline for proactive threat detection
- Code Level Protection to secure AI from inception to deployment
- MultiModal Guardrails to protect vision and language models
- AI Augmented Reports for actionable insights into AI operations
- Input validation and output filtering to ensure compliance
- Honeypots to detect prompt injection attempts
- Sandboxing environments for analyzing untrusted data