AI threat detection definition
AI threat detection involves the application of artificial intelligence (AI) and machine learning (ML) algorithms to identify, analyze, and respond to cyber threats in real time. This technology helps identify complex and emerging threats and enhance the capacity to protect sensitive data and systems from malicious actors.
See also: AI tool, anomaly-based detection
How AI threat detection works
- AI threat detection systems continuously monitor vast amounts of data across endpoints, networks, and cloud environments.
- These systems use advanced machine learning models to analyze network traffic, user behavior, system logs, and file characteristics.
- Supervised learning models are trained on labeled datasets of known threats and benign activities to recognize established attack patterns and signatures.
- Unsupervised learning excels at detecting anomalies and deviations from normal behavior, allowing the identification of novel or zero-day threats that lack predefined signatures.
AI threat detection benefits
- Increased detection speed and efficiency.
- Rapid response and threat mitigation, often in real time.
- Improved detection accuracy and a reduction in false positives.
- Proactive and predictive security, helping identify potential weaknesses before they escalate into attacks.
- Ability to quickly adapt and evolve, which aids in recognizing new attack methodologies, including sophisticated threats like malware, ransomware, zero-day vulnerabilities, supply chain attacks, and account hijacking.
