SAAF AI Connector Acceptance Package

Purpose: Evaluate and document the security, privacy, and compliance readiness of AI connectors before production use—ensuring responsible integration with enterprise systems and sensitive data.

Why Use This?

AI connectors enable powerful automation and search capabilities—but they also introduce new risks. This package helps your security team:

• Understand what data flows through the connector

• Identify risks to privacy, compliance, and internal controls

• Mitigate threats like data leakage, bias, phishing, and unauthorized access

• Ensure safe, scalable, and ethical deployment

Use it for reviewing:

• AI connectors from platforms like Atlassian, Microsoft, Google, Salesforce, etc.

• Any app or integration that uses generative AI or large language models (LLMs)

• Tools that access or exchange sensitive data through APIs or embedded assistants

1. Executive Summary

• Connector Name & Provider:

• Business Use Case:

• Deployment Goal: How this connector supports organizational needs (e.g., improves productivity, enables federated search, etc.)

• Internal Teams Impacted: (e.g., Legal, Finance, Customer Support)

2. Connector Overview

Field	Description
Connector Function	What it does (e.g., indexes Google Drive, syncs tasks, summarizes tickets)
AI Type	Generative AI, RAG (Retrieval-Augmented Generation), Predictive AI
Model Type	Proprietary, Open Source, Hosted LLM (e.g., GPT-4, Gemini)
Deployment Model	SaaS, On-premise, Hybrid
Data Flow Summary	High-level explanation of what data is sent/received and by whom

3. Data Privacy and Protection

3.1 Data Handling

• What types of data are processed (e.g., PII, client records, financials)?

• Are sensitive fields masked, filtered, or encrypted?

• Does the connector use data for AI model training?

3.2 Data Residency & GDPR

• Where is the data processed and stored?

• Does the vendor support data residency options (e.g., EU, US)?

• Is the vendor GDPR-compliant?

• DPA signed? SCCs in place?

3.3 Data Retention & Deletion

• Can data be deleted upon request (Right to Erasure)?

• What is the data retention policy?

• How is temporary cache or session data handled?

4. AI Model and Risk Mitigation

4.1 Model Transparency

• Is the model explainable or observable?

• Are there known issues with hallucinations, bias, or misalignment?

• Can admins restrict which models are used?

4.2 Human-in-the-Loop Controls

• Are critical outputs (e.g., financial summaries, customer emails) reviewed by a human?

• How are inaccuracies or inappropriate outputs reported and escalated?

5. Security Architecture

5.1 Authentication and Authorization

• Supports SSO and MFA?

• RBAC controls in place?

• Can permissions be scoped per department or team?

5.2 Encryption

• End-to-end encryption standards (TLS 1.2+, AES-256)?

• Are encryption keys managed internally or by vendor?

5.3 Logging & Monitoring

• Can activity logs be exported to a SIEM?

• Are audit logs accessible to security teams?

• Are alerts available for anomalous behavior?

6. Fraud, Phishing, and Misuse

• Does the connector generate communications (emails, chats)?

• Could it be used to impersonate internal users?

• Protections against:

  • Prompt injection?

  • Social engineering?

  • Fake or misleading outputs?

• Are there escalation protocols for flagged content?

7. Review and Testing Plan

7.1 Access Review

• Who will use the connector?

• Is access managed via IT or self-service?

• Is there a deprovisioning process?

7.2 Testing Environments

• Was the connector tested in a staging/sandbox environment?

• Was synthetic or masked data used?

• Any issues surfaced during testing?

7.3 Risk Rating Summary (Example Table)

Category	Risk Level	Notes / Mitigation Steps
GDPR Compliance	Medium	SCC in place, residency confirmed
Prompt Injection	High	Human review + prompt hardening needed
Phishing / Spoofing Risk	Medium	Connector disabled for chat generation
PII/Data Exposure	Low	Masking and RBAC enforced

8. Required Supporting Documentation

• Data Flow Diagram

• Data Processing Agreement (DPA)

• DPIA (Data Protection Impact Assessment), if applicable

• AI Risk Audit or Model Behavior Testing Summary

• Access Control and User Provisioning Plan

• Logging & Monitoring Overview

• End User Guidelines / Terms of Use

• User Training or Prompt Safety Guide

• Incident Response Plan for AI misuse

Appendix (Optional)

• Vendor security certifications (e.g., SOC 2, ISO 27001)

• Internal ethics or bias review

• Recent penetration test or red team report

• Change management or rollback procedures

• Vendor support or security contact