threat-detection
Safeguard articles tagged "threat-detection" — guides, analysis, and best practices for software supply chain and application security.
35 articles
AI cybersecurity hub (AI-driven threat detection)
Prisma Cloud's AI-driven detection watches runtime behavior, but AI in cybersecurity still misses supply chain attacks like XZ Utils. Provenance matters more.
Malicious Code Meaning: A Practical Definition for Developers
Malicious code is any software written to damage, disrupt, or gain unauthorized access to a system. Here is what the term actually covers and how it reaches your stack.
How To Handle a Zero-Day Vulnerability: A Response Playbook
A zero-day has no patch on day one, so your first move is containment and exposure reduction, not waiting on a vendor fix. Here is how to handle a zero-day vulnerability under pressure.
Cloud Security Intelligence: How to Turn Signals Into Action
Cloud security intelligence is the practice of correlating raw telemetry from your cloud accounts into prioritized, actionable risk. Here is how to build it without drowning in alerts.
What Is Malicious Code in Cyber Security? Types, Detection, and Defense
Malicious code is any software written to harm a system or its users. Here is how the main families work, where they hide in modern supply chains, and how to catch them.
What is Malware
Malware now hides in open source packages and CI pipelines, not just email attachments. Here's what it is, how it spreads, and how to catch it early.
What Is AI-Powered Cybersecurity? A Practical Security Guide
AI-powered cybersecurity means using machine learning and language models to detect, triage, and remediate threats faster than rule-based tooling alone. Here is where it genuinely helps and where the hype outruns reality.
AI Cybersecurity Software: What It Actually Does and Where It Falls Short
AI cybersecurity software uses machine learning to spot anomalies, triage alerts, and prioritize risk faster than humans can. Here is what it genuinely helps with and where it still needs a human.
What is Adversarial Machine Learning
Adversarial machine learning exploits model decision boundaries via evasion, poisoning, extraction, and inference attacks -- here's how it works and how to defend against it.
Lateral movement
A precise breakdown of what lateral movement is, the MITRE ATT&CK techniques and pivoting methods attackers use, and how to detect them before they spread.
Living off the land (LOTL) techniques
A precise breakdown of living off the land (LOTL) attacks: how LOLBins, fileless malware, and dual-use tool abuse let intruders hide in plain sight.
How to set up AWS GuardDuty for threat detection
A step-by-step guide to enabling AWS GuardDuty across accounts and regions, routing findings to your alerting stack, and triaging results.
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