Prevent AI Threats at the Endpoint (AI-EDR)

Use Case

Extend endpoint detection and response into the local runtime of autonomous AI. Discover endpoint agents and Shadow MCP, map capabilities and access, inspect prompts and tool traffic, and evaluate proposed actions before they become operating-system or enterprise activity.

Challenges

Traditional EDR identifies processes and system events but cannot reliably interpret agent intent or prompt-driven behavior. Autonomous endpoint AI introduces new blind spots:

• Local agents can read files, execute commands, and call APIs
• Shadow MCP servers create untracked tools and connections
• Broad permissions expose credentials and enterprise resources
• Malicious prompts can trigger unsafe actions and data leakage

Solution

PointGuard AI provides a comprehensive AI-EDR solution for discovering endpoint AI and stopping unsafe behavior before execution:

1. Use flexible discovery options. Deploy an Endpoint Client through MDM, integrate agentlessly with Microsoft Intune, or use a browser plugin for Shadow AI visibility and controls.

2. Map capabilities and access. Use Agentic Endpoint Security to connect agent identities with permissions, files, applications, APIs, tools, skills, and enterprise resources.

3. Intercept proposed actions. Evaluate commands, API requests, and file modifications inside the cognitive loop before they execute.

4. Block threats and data loss. Inspect prompts, instructions, responses, and tool traffic for prompt injection, malicious instructions, credentials, and sensitive information.

AI-EDR creates an enforceable runtime boundary for autonomous AI on managed macOS, Windows, and Linux endpoints.

Risks Addressed

Applicable framework risks and controls include:

OWASP Top 10 for LLMs

• LLM01:2026 Prompt Injection
• LLM02:2026 Sensitive Information Disclosure
• LLM03:2026 Excessive Agency
• LLM04:2026 Supply Chain
• LLM10:2026 Improper Output Handling

OWASP Top 10 for Agentic Applications

• ASI01: Agent Goal Hijack
• ASI02: Tool Misuse and Exploitation
• ASI03: Identity and Privilege Abuse
• ASI04: Agentic Supply Chain Vulnerabilities
• ASI05: Unexpected Code Execution (RCE)
• ASI10: Rogue Agents

NIST AI Risk Management Framework

• GOVERN 1.4: Transparent risk policies, procedures, and controls are established
• MAP 1.1: Intended use, context, users, and lifecycle risks are documented
• MEASURE 2.7: AI system security and resilience are evaluated and documented
• MANAGE 4.1: Post-deployment monitoring and change management are implemented