AI Security Posture Management

AI Security Posture Management (AI-SPM) provides comprehensive visibility into the AI components running across your cloud and SaaS environments, including models, agents, training datasets, and model endpoints. By integrating natively with the Cortex data classification and DSPM engines, AI-SPM identifies where sensitive data is used in training and inference (runtime), allows you to monitor data lineage, and secures the agents interacting with your systems.

AI pipeline mapping and visibility

AI-SPM discovers and visualizes the full AI stack to eliminate visibility gaps in both infrastructure and data.

• Unified inventory: Maintain a comprehensive list of models, agents, and endpoints across AWS, Azure, GCP, and connected SaaS applications.
• Context-aware discovery: Automatically identify AI assets, including newer storage types like Amazon S3 Vectors, and the sensitive training or inference data they use by leveraging native data classification and DSPM engines.
• AI Security dashboard: Monitor your entire AI ecosystem from a centralized dashboard that provides a unified view of security posture, findings, and risks across cloud and SaaS AI entities.
• AI Bill of Materials (AI-BOM): Track AI software packages and development dependencies to identify vulnerabilities within the AI supply chain.

AI-centric risk and threat detection

AI-SPM monitors for infrastructure vulnerabilities and AI-specific threat vectors that traditional security tools often overlook.

• Sensitivity-based prioritization: Prioritize the remediation of misconfigured endpoints and insecure setups based on the sensitivity of the data (such as PII or PCI) being processed.
• Comprehensive AI threat defense: Secure your AI ecosystem from development to runtime. Proactively detect risks like data poisoning and model inversion vulnerabilities, while leveraging integrated Data Detection and Response (DDR) to monitor AI data flows and block active, real-time data exfiltration or unauthorized access to sensitive training sets.
• Custom risk rules: Codify internal security policies into the risk engine by creating custom rules to identify organization-specific AI misconfigurations.
• Agent governance: Identify over-privileged or high-risk AI agents, including both cloud and SaaS-based agents, by monitoring their access to sensitive data and tools.

Governance and standards alignment

AI-SPM measures and enforces your organization's AI security posture against established regulatory frameworks.

• Industry frameworks: Automated mapping to standards such as the EU AI Act.
• Policy guardrails: Apply consistent security policies to govern the use of both sanctioned and third-party AI applications across the enterprise.

Note: For more information, see the Cortex Cloud AI Security documentation.