Secure & Ethical Use of Generative AI and Prompt Engineering

Learn prompt-based data leakage risks and how to apply guardrails to protect confidential information when using gen AI, including data in transit and the roles of GDPR and HIPAA.

Mitigate prompt leaks by applying four strategies: stay vague, avoid personal information, use placeholders, and follow company guidelines, with practical examples for secure Gen AI use.

Explain three output-based data leakage paths—prompt reflection, contextual disclosure, and internal exposure—and explore prompt chaining and context window effects, with mitigation strategies for secure outputs.

Explore four practical strategies to mitigate output leaks in Gen AI, including review and redact, sanitize output, manage context windows with fresh threads, and securely store outputs with guardrails.

Explore how prompt injections and malicious prompts trick gen AI models, expose confidential data, and threaten security, privacy, and ethics, with practical safeguards for employees.

Explore shadow ai and its security implications as unapproved tools access inbox data, meetings, and client information; learn why whitelisting and governance protect privacy and ethics.

Cross-account risk arises when employees sign in to Gen AI tools like ChatGPT with personal emails; use official work emails and avoid signing in from home computers to protect data.

Define ethical ai use as fair, honest, transparent, and non-harmful, outlining the principles of fairness, accountability, transparency, responsibility, and privacy with practical safeguards.

Examine AI hallucinations, where ChatGPT-like models produce plausible yet false outputs, and study real-world cases like Air Canada and EEOC to highlight the need for human verification.

Explore five AI hallucination types—fabrication, agreement bias, prompt contradiction, image hallucination, and code hallucination—with concrete examples. Learn why hallucinations are unpredictable and how ongoing model updates affect reliability.

Explore practical strategies to minimize AI hallucinations, including prompt engineering, self-audit prompts, chain-of-thought prompting, and providing data citations to improve accuracy.

Promote transparency by attributing AI use, check originality with corporate tools, paraphrase and edit for authenticity, and add personal insights to avoid AI misrepresentation and protect data privacy.

Stay aware of prompt leaks, data leaks from inputs and outputs, and prompt injections; apply organization guidelines and Gen II guidelines to safeguard privacy, ethics, and security.