Editing AI for Cybersecurity (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Cybersecurity is fundamentally an adversarial game: attackers continuously adapt to evade defenses, making static rules quickly obsolete. AI enables adaptive defenses that can identify novel attack patterns from behavioral signals rather than fixed signatures.

**Signature vs. behavior-based detection**: Traditional antivirus uses signatures (hashes of known malware). It fails on zero-days and polymorphic malware. Behavioral detection uses ML to identify malicious patterns of behavior (process injection, lateral movement, data exfiltration) regardless of specific implementation. This catches novel threats but produces more false positives.

**The kill chain and AI coverage**: The MITRE ATT&CK framework documents attacker tactics and techniques across the attack lifecycle: Initial Access → Execution → Persistence → Privilege Escalation → Defense Evasion → Credential Access → Discovery → Lateral Movement → Collection → Exfiltration → Impact. AI can be applied at each stage, but attackers operate across the full chain.

**Graph-based threat detection**: Network activity forms a graph (devices, users, processes as nodes; connections and data transfers as edges). Graph neural networks and graph analytics detect lateral movement patterns, command-and-control infrastructure, and malware propagation that are invisible when analyzing events in isolation.

**The LLM security frontier**: LLMs enable more sophisticated spear-phishing at scale, automated vulnerability discovery, and social engineering. Simultaneously, LLMs assist defenders with log analysis, report generation, threat intelligence synthesis, and code vulnerability detection.
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