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AI cybersecurity involves practices to secure AI data, models and usage. Practices include red team exercises (ethical hacking), threat hunting, vulnerability assessments and monitoring to root out cases of shadow AI (the unsanctioned use of AI tools by employees). It also includes implementing isolation controls and a zero-trust access model for AI systems; safeguarding training data and models against attacks; and securing the underlying infrastructure. It also includes implementing input validation and content guardrails to prevent sensitive information leakage.

AI strengthens cybersecurity by enabling the rapid identification of cyberattacks, enhancing incident response capabilities and improving user authentication. It searches for characteristics of malware, phishing and other attack methods by analyzing network traffic, suspicious login attempts, IoT device activity and other indicators of compromise. It helps detect rogue or compromised endpoints, stops malware and isolates compromised users. It enables faster, more accurate threat analysis and improves data protection by classifying sensitive data and optimizing encryption and tokenization processes.

It enhances IAM by analyzing risk factors in real time, such as login patterns, requesting access to unknown devices and systems, or unusual data downloads. It identifies high-risk behavior and can block access, require additional verification, or alert an admin for further action. It can also identify and remove outdated and overly-broad permissions, making the organization less vulnerable to stolen credentials or accidental exposure.

ML algorithms democratize cybercrime by reducing the skill required to commit offenses. For example, a would-be phisher no longer needs to write polished English and develop a believable fake message; they can simply ask a ML tool to create one for them.