LLM Risks & Mitigation Strategies
Understanding hallucinations, copyright, privacy, safety, governance, and responsible AI practices.
Understanding hallucinations, copyright, privacy, safety, governance, and responsible AI practices.
Large Language Models offer transformative capabilities but are accompanied by significant risks that impact trust, legality, ethics, and safety.
This page breaks down those risks and provides practical mitigation strategies for safe and responsible deployment.
LLMs may produce false, misleading, or fabricated information.
Generated content may resemble copyrighted data used in training.
Risk of exposing sensitive data if models memorize or leak training inputs.
LLMs can unintentionally generate harmful, biased, or unsafe outputs.
Lack of standardized rules, monitoring, and transparency in model usage.
Evaluation, alignment, filtering, monitoring, and human oversight.
Assess hallucinations, leakage, harm, and biases.
Filtering, alignment, and privacy controls.
Evaluate outputs and monitor for issues.
Use policies, audits, and transparency reports.
Yes, because they predict text statistically, not factually.
They can memorize patterns from training if not properly filtered or trained.
Safety alignment, content filtering, oversight, and continuous testing.