From Rule-Based to AI-Driven
Traditional security automation relied heavily on predefined rules and signatures. While effective for known threats, this approach struggled with:
- Zero-day attacks - Unknown threats that bypass signature-based detection
- Advanced persistent threats (APTs) - Sophisticated, multi-stage attacks
- Polymorphic malware - Threats that change their code to evade detection
- False positive management - Overwhelming security teams with irrelevant alerts
AI-powered automation addresses these limitations through:
- Machine learning models that adapt and learn from new data
- Behavioral analysis that detects anomalies in user and system behavior
- Contextual intelligence that considers multiple data sources for decision-making
- Predictive capabilities that anticipate potential threats before they manifest