Secure GenAI Applications from Code Commit to Runtime
Innovate with confidence and speed while protecting your Generative AI applications from the top security risks associated with Large Language Models (LLMs).
Protect LLM Apps from Code Commit to Runtime
Secure LLM applications across the entire development lifecycle. Protect against revenue and reputation risks from threats such as unexpected behaviors from the LLM application output that initiates attacks. Using runtime context, trace security risks back to the precise line of code to enable developers to fix issues efficiently.
Identify LLM Risks Early
Scan the application code to detect LLM components and identify security gaps as per the OWASP Top 10 LLM Risks, such as unauthorized data access, misconfigurations and LLM-specific vulnerabilities.
Block Risky Code with Assurance Policies
Detect LLM-related risks in the pipeline. Configure GenAI specific assurance policies to prevent unsafe use of (known risk) LLMs with compromised code. Save time and resources on incident remediation by setting up guardrails and establishing risk thresholds to prevent risky code from being deployed into production.
Secure AI Workloads with AI-SPM
Discover and gain full visibility for LLM components being used across all your cloud environments. Safeguard AI applications in your cloud environment from vulnerabilities and misconfigurations. Get proactive with mitigating AI-related risks and ensure your LLM-powered applications are meeting the latest compliance standards.
Leverage GenAI for Faster Remediation
Using the power of generative AI, get clear, concise instructions for fixing misconfigurations and vulnerabilities across container images and other artifacts. reading advisories, searching for patches and building verification steps.
