AI
AI in security operations: separating automation myths from reality
Machine learning models accelerate triage in security operations centers, yet they routinely obscure the underlying logic of network anomalies.
threat detection
Everything TechSec Observer has reported about threat detection: 8 stories, newest first. Part of our Threat Intelligence coverage.
Threat Intelligence
Active Directory Attack Warning Signs Early Detection Methods
Credential theft often leaves no login trace, so you must monitor process memory access patterns to detect silent compromise before lateral movement begins.
Threat Intelligence
What is Dwell Time: Why Detection Latency Dictates Breach Severity
Shortening the period between intrusion and detection reduces the window for lateral movement and data exfiltration more effectively than perimeter hardening alone.
Threat Intelligence
Purple Team Cyber Security: Bridging Red and Blue for Faster Defences
Purple teaming collapses the delay between detection and response by forcing defenders to adapt tactics while attackers are still active.
Threat Intelligence
Process Injection Prevention Checklist for Secure Code
Memory layout randomisation alone fails against injection because the attacker controls the target process execution context and writes directly into allocated space.
Data Breaches
How Data Exfiltration Works: The Silent Stages of Theft
Attackers rarely break walls; they wait for you to leave the door open by abusing trusted channels and normalising small data transfers that bypass standard security checks.
AI
Implementing AI-Assisted Threat Detection: A Practical Rollout
Machine learning models detect anomalies by recognising deviations from established baselines, yet they require rigorous data hygiene to avoid amplifying noise into false alarms.
Threat Intelligence
User and Entity Behavior Analytics: Definition, Purpose and Mechanics
Move beyond static rules by learning how UEBA establishes behavioural baselines to detect subtle anomalies that traditional controls miss.