Auth Strategies

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Overview

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The AI Auth Challenge

• Direct user interaction with services
• Users understand what they’re authorizing
• Sessions are short-lived and contextual

• The AI makes requests on behalf of users
• Users may not understand what the AI will do
• Sessions span multiple conversations and contexts
• The AI might combine multiple services unexpectedly

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Auth Strategies for AI

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1. OAuth 2.0 with AI Extensions

• Scope Clarity: Make scopes human-readable since users authorize AI access
• Time Limits: Add temporal bounds to permissions
• Audit Trails: Log what the AI actually did with the access

User → "AI, check my GitHub PRs"
AI → "I need GitHub access, please authorize: [OAuth Link]"
User → Authorizes specific repos
AI → Uses token to check PRs

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2. Rich Authorization Requests (RAR)

{
  "type": "file_access",
  "locations": ["/project/docs"],
  "actions": ["read"],
  "purpose": "Summarize documentation",
  "max_duration": "1h"
}

• Context-aware permissions
• Purpose-bound access
• Granular control

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3. Pre-Consent Patterns

• Template-Based: “AI can always read my calendar for scheduling”
• Conditional: “AI can send emails if I explicitly say ‘send’”
• Bounded: “AI can make API calls up to 100/day”

• Store consent rules in user profiles
• Check consent before each action
• Provide clear consent management UI

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4. Reusable Authentication

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Token Vaulting

• Encrypt tokens at rest
• Implement token refresh logic
• Provide clear token inventory UI

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Delegated Auth

• Platform maintains service connections
• Users authorize the platform once
• Platform manages token lifecycle

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Session-Based Auth

• Auth persists for conversation duration
• Clear session boundaries
• Explicit session termination

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Security Considerations

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Token Leakage

• Never expose tokens in AI responses
• Implement token redaction in logs
• Use short-lived tokens when possible

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Scope Creep

• Regularly audit AI permission usage
• Implement least-privilege defaults
• Provide usage analytics to users

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Confused Deputy

• Validate the AI is acting for the right user
• Implement request signing/verification
• Maintain clear auth context

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Best Practices

1. Transparency First: Users should always know what the AI can access
2. Explicit Consent: Never assume permission for sensitive actions
3. Granular Control: Allow users to grant/revoke specific permissions
4. Time Boundaries: Implement automatic permission expiration
5. Audit Everything: Maintain detailed logs of AI actions

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In Civic Labs

• OAuth Integration: Built-in OAuth flow handling
• Token Management: Secure storage and refresh
• Auth Context: Maintains user context across MCP calls
• Permission UI: Clear visualization of granted permissions

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Implementation Patterns

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Pattern 1: Just-In-Time Auth

async function callTool(tool: string, params: any) {
  const token = await getTokenOrPromptAuth(tool);
  return await executeWithToken(tool, params, token);
}

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Pattern 2: Batch Pre-Auth

// At conversation start
const requiredScopes = analyzeConversationNeeds();
const tokens = await requestBatchAuthorization(requiredScopes);

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Pattern 3: Progressive Auth

// Start with read-only
let permissions = ['read'];

// Escalate as needed
if (userRequests.includes('modify')) {
  permissions = await requestAdditionalScopes(['write']);
}

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Next Steps

• Explore MCP Hub auth implementation
• Learn about Guardrails for auth policies
• Understand the Model Context Protocol