BotNet News

Amid a proliferation of attacks that expand the attack surface, organizations must adopt AI cybersecurity to detect and mitigate risks. These technologies automate processes that safeguard data and systems from unauthorized access, credential theft, and other attacks.

AI security platforms use machine learning to detect patterns and anomalies based on data. They also identify potential threats to sensitive information and take preventive actions. They can protect against a range of attacks, including ransomware, phishing, and identity theft. They can also detect threats to critical infrastructure. They can help ensure compliance with regulations like GDPR and NIS2.

To get the most out of their AI cybersecurity investments, CISOs should choose tools that prioritize measurable results rather than theoretical capabilities. They should focus on reducing tool sprawl, increasing risk visibility, and embedding security into developer workflows without slowing down development.

For example, Lakera Guard prioritizes alerts on real threats and minimizes noise with smart filtering to save analysts from the fatigue of chasing false positives. Their security platform is also flexible and allows organizations to customize their own policies based on the specifics of their threat environment.

Another company that uses AI to fight cyberattacks is Zscaler, which combines three decades of firewall craft with fresh, adaptive machine learning. Its global network sees trillions of daily signals and enables sophisticated bot detection. Its Zero Trust approach flips the castle-and-moat model to let remote workers work securely, yet keep their applications and data boxed in with secure telemetry and security controls that adapt on the fly.