Authorize MCP Tool Access

Use Case

Place identity-aware authorization directly in the MCP path so every tool call is evaluated before execution. Apply granular policy by agent, user, tool, operation, purpose, resource, data sensitivity, environment, and risk.

Challenges

MCP connects agents directly to powerful enterprise tools and data. Broad or static access creates several control gaps:

• Server access can expose every tool behind it
• Agent permissions may exceed the represented user’s authority
• Machine-speed tool chains amplify unsafe actions quickly
• Session-level approval misses risk in individual operations

Solution

PointGuard AI provides a comprehensive solution for least-privilege MCP tool authorization:

1. Establish identity context. Identify the agent and relevant user or workload before it requests access to enterprise tools.

2. Define granular permissions. Set policies for specific tools, operations, resources, and workflow conditions rather than broad server access.

3. Evaluate every tool call. Use MCP Security Gateway to assess identity, intent, purpose, context, data sensitivity, and action risk before execution.

4. Escalate high-risk actions. Block disallowed requests or require human approval and step-up authentication when policy thresholds are exceeded.

MCP becomes a governed access layer instead of an implicit trust channel for autonomous activity.

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
• 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
• 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