AI Safety

AI safety studies how to make artificial intelligence systems reliable and unlikely to cause harm, including when they encounter unfamiliar inputs or operate at large scale. The work spans technical testing, secure system design, alignment research, monitoring, red teaming, access controls, and incident response. Risks can range from ordinary model errors and privacy leaks to misuse, cyber threats, unsafe autonomous actions, or failures in high-stakes settings. Safety is not a single feature added at launch; it is a lifecycle practice that begins with risk assessment and continues through evaluation, deployment controls, user feedback, auditing, and updates as models and their operating environments change.