Mimecast Cybergraph

Attackers constantly evolve their tactics to side-step traditional defenses, making it nearly impossible for IT security teams to fight off cyberattacks without the aid of artificial intelligence. By constantly ‘learning’ an organization’s environment and user behaviors to get smarter over time, AI tools create a baseline of normal, creating detections and alerts for anomalous behavior.

As AI matures and enterprises adopt it more broadly, threat actors are taking advantage: they can employ techniques like data poisoning to infect these systems and influence their output. And, because humans can introduce bias into AI models in a number of ways, cybercriminals can leverage flaws in a biased AI system. That’s why IT security teams must avoid relying solely on AI to detect threats.

Machine learning algorithms can help AI become more resilient. Using tactics such as training AI on unique data, analyzing patterns of errors in training data, and thinking like an adversary, organizations can use machine learning to make their AI models more resilient to attacks. Adding a new layer of defense – blocking hard-to-detect threats while alerting and detecting anomalous behavior – is critical.