How it typically works?
- Signature scanning: traditional AV checks files against a database of known threats.
- Heuristic analysis: unknown files are analyzed for suspicious patterns.
- Behavioral monitoring: NGAV observes runtime behavior to catch novel threats.
- Machine learning models: classify files or processes as malicious based on learned patterns.
- Cloud updates: threat intelligence is continuously updated from vendor networks.
Common techniques
- Signature-based detection: effective against known malware families.
- Heuristics: detect suspicious code structures even without exact matches.
- Behavioral analysis: block ransomware when encryption patterns are detected.
- Machine learning: train models on malicious vs benign data to improve accuracy.
- Sandboxing: detonate files in isolated environments to observe behavior.
- Integration with EDR/XDR: NGAV often forms the prevention layer while EDR handles investigation.
Impact
Antivirus remains important for baseline protection, especially on consumer and unmanaged devices. NGAV improves resilience against zero-day malware and ransomware, complementing broader endpoint defense strategies. However, no AV system is foolproof, and attackers actively develop evasion techniques.
For SecOps, NGAV provides valuable protection but must be integrated with detection and response tools for full visibility and control.
Further reading
- NIST: Malware Detection. Read more
- CrowdStrike: NGAV explained. Read more
- Sophos: What is NGAV? Read more
- SentinelOne: NGAV vs EDR. Read more
- McAfee: The evolution of antivirus. Read more