“Part of my research in Large Language Model (LLM), RAG Risks Scenarios”
Retrieval Augmented Generation (RAG) combines a generative model with external knowledge to generate better responses.
Hare are some of RAG Risk Scenarios:
Data Privacy & Security Risks:
Sensitive data might be exposed during the generation process. External queries may unintentionally retrieve private information.
Bias & Misinformation:
If the sources are biased or inaccurate, responses may reflect or amplify misinformation.
Compliance & Regulatory Concerns:
RAG systems might retrieve info that conflicts with regulations or copyright laws, risking legal issues.
Hallucination & Fabrication:
Generative models might create fictional or incorrect information, blending retrieved and generated data unpredictably.
Manipulation of Sources:
Malicious actors could alter external sources to influence RAG outputs or skew responses.
Operational & Technical Risks:
Real-time data retrieval can cause latency, downtime, or security vulnerabilities.
Ethical Concerns:
Over-reliance on specific sources can lead to limited perspectives and a lack of transparency.
Now, Securing RAG systems requires addressing these risks at every level:
Data Privacy:
Use encryption, RBAC and MFA to secure sensitive info. Anonymize or pseudonymize PII to protect privacy.
Source Validation:
Ensure sources are reputable. Maintain a whitelist of trusted data and use digital signatures to verify integrity.
Bias Mitigation:
Use diverse sources and bias detection tools to reduce misinformation.
Compliance:
Regularly audit compliance with regulations like GDPR and respect copyright laws.
Hallucination Prevention:
Cross-check retrieved data and provide transparency tools to users.
Adversarial Prevention:
Sanitize external data to avoid malicious attacks and monitor for unusual patterns.
Technical Safeguards:
Use rate limiting, failover systems, and optimize retrieval processes to reduce latency and disruption.
Ethical Safeguards:
Ensure transparency and human oversight for high stakes outputs. Monitor for biases and performance issues.
Governance:
Develop governance frameworks, assign accountability and maintain incident response plans for security and compliance.
