Harden AI Systems with Security Posture Management

Use Case

AI development platforms introduce configuration, identity, data, and supply-chain risks that traditional cloud posture tools may not fully understand. PointGuard AI provides AI-specific posture management across MLOps and AI infrastructure so teams can find and remediate weaknesses before they become attack paths.

Challenges

The AI environment creates several recurring security and governance challenges:

• AI platforms create rapidly changing configuration surfaces
• Unsafe permissions expose models, data, and credentials
• Malicious components enter notebooks and supply chains
• General cloud posture tools lack AI-specific risk context

Solution

PointGuard AI provides a comprehensive solution for continuously assessing AI platforms, permissions, and supporting infrastructure:

1. Connect AI development platforms. Use AI Security Posture Management and AI Discovery & Inventory to integrate AI and MLOps environments so posture can be evaluated across cloud-native AI services and development workflows.

2. Detect AI-specific misconfigurations. Identify configuration errors, exposures, unsafe access policies, unauthorized changes, and other AI infrastructure risks.

3. Prioritize remediation. Use AI-specific security context to focus teams on the weaknesses most likely to create material exposure.

4. Automate corrective workflows. Route findings into remediation and notification workflows so security issues can be tracked and resolved efficiently.

Continuous AI posture management reduces configuration drift and helps keep rapidly changing AI development environments secure and governable.

Risks Addressed

Applicable framework risks and controls include:

OWASP Top 10 for LLMs

• LLM02:2026 Sensitive Information Disclosure
• LLM04:2026 Supply Chain
• LLM05:2026 Data and Model Poisoning
• LLM09:2026 Vector and Embedding Weaknesses

OWASP Top 10 for Agentic Applications

• ASI03: Identity and Privilege Abuse
• ASI04: Agentic Supply Chain Vulnerabilities
• ASI05: Unexpected Code Execution (RCE)
• ASI06: Memory & Context Poisoning
• ASI07: Insecure Inter-Agent Communication

NIST AI Risk Management Framework

• GOVERN 6.1: Policies and procedures address third-party AI risks
• MAP 5.1: Likelihood and magnitude of identified impacts are documented
• MEASURE 2.7: AI system security and resilience are evaluated and documented
• MANAGE 1.1: Determine whether deployment should proceed
• MANAGE 4.1: Post-deployment monitoring and change management are implemented