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Module 12 · Page 13 of 20 · 10 sections
Security, Privacy and Responsible AI
Risks specific to AI systems, how to protect data and prompts, guardrails for LLM apps, and the principles and regulations that frame them.
1. Introduction
- As AI systems become more powerful and widely used, security, privacy and responsible AI practices are essential to ensure safe, fair and trustworthy systems.
- AI systems handle sensitive data, make decisions that affect people and can be misused if not properly designed and controlled.
- This topic covers key risks, best practices and frameworks to build secure, private and responsible AI applications.
2. Key Terms
- Data Security
- Protecting data from unauthorized access and attacks.
- Privacy
- Ensuring user data is handled responsibly.
- PII
- Personally Identifiable Information: information that can identify a person (name, email, phone, etc.).
- Responsible AI
- Building fair, transparent and accountable AI systems.
- Bias
- Unfair or discriminatory behavior in AI models.
- Explainability
- Ability to understand and explain model decisions.
- Governance
- Policies and processes to manage AI risk.
- Compliance
- Following legal and regulatory requirements.
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3. Security Risks in AI Systems
- Data leakage
- Sensitive data exposed in prompts or logs.
- Prompt injection
- Malicious input to manipulate the LLM.
- Model theft
- Stealing proprietary models or weights.
- Adversarial attacks
- Manipulating input to get wrong output.
- Insecure plugins / tools
- Exploiting external tools or APIs.
- Supply chain risks
- Vulnerabilities in open-source models or libraries.
- Uncontrolled data access
- Leading to sensitive data exposure.
- Insecure deployment
- Misconfigured cloud, API or network.
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4. Privacy Risks and Challenges
- 1Exposure of PII in training or inference data.
- 2Storing user conversations without consent.
- 3Sharing data with third-party APIs (e.g. LLM providers).
- 4Re-identification risk from anonymized data.
- 5Data retention and deletion policies.
- 6Cross-border data transfer and compliance.
- 7Use of user data for model training without permission.
- 8Lack of transparency in data usage.
5. End-to-End Security Architecture
UserAuthenticated
API GatewayAuthentication, rate limiting, input validation, encryption (HTTPS)
Application ServerAccess control, prompt filtering, audit logs, secure tool access
LLM / AI ServiceContent filtering, security guardrails, monitoring, no sensitive data storage
Secure database (encryption at rest) · Audit & monitoring (logs, alerts) · Secrets management (API keys, tokens)
6. Data Privacy Techniques
- Data Anonymization
- Remove or mask PII.
- Pseudonymization
- Replace identifiers with aliases.
- Differential Privacy
- Add noise to protect identity.
- Data Minimization
- Collect only necessary data.
- Encryption
- In transit (TLS) and at rest.
- Access Control
- Role-based access (RBAC).
- Data Retention Policy
- Store data only as required.
- User Consent
- Clearly inform and get permission.
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7. Responsible AI Principles
- Fairness
- Avoid bias and discrimination.
- Transparency
- Explain how the model works.
- Accountability
- Take responsibility for model outcomes.
- Privacy
- Protect user data and respect consent.
- Safety
- Prevent harmful or misleading outputs.
- Human-in-the-loop
- Keep humans in critical decisions.
- Inclusiveness
- Ensure accessibility for all users.
- Sustainability
- Minimize environmental impact.
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8. Guardrails for LLM Applications
- Input filtering
- Detect and block harmful content.
- Output filtering
- Prevent unsafe, biased or toxic responses.
- PII detection
- Remove sensitive information from prompts and responses.
- Prompt sanitization
- Prevent prompt injection attacks.
- Content moderation
- Use safety filters (OpenAI, Llama Guard, etc.).
- Tool access control
- Restrict external API and tool usage.
- Rate limiting
- Prevent abuse and DoS attacks.
- Monitoring and alerting
- Track suspicious activities.
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Related06LLM System Design
9. Compliance and Regulations
- GDPR (EU)
- Data protection and privacy rights.
- CCPA (California)
- User data privacy and control.
- HIPAA (Healthcare)
- Protection of medical data.
- DPDP Act (India)
- Personal data protection law.
- AI Act (EU)
- Rules for safe and trustworthy AI.
- Industry specific compliance
- Finance, healthcare, etc.
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10. Best Practices
- 1Never expose sensitive data in prompts.
- 2Use encryption and access control.
- 3Implement input and output filtering.
- 4Follow data minimization and retention policy.
- 5Regularly monitor and audit system behavior.
- 6Test for bias, fairness and security issues.
- 7Keep models and dependencies up to date.
- 8Provide clear user communication and consent.