Introduction
As cyber threats continue to evolve in sophistication and scale, defending modern enterprises can no longer rely on a single detection technique or control. Over the past decade, attackers have adapted faster than traditional security architectures—moving beyond commodity malware into phishing, business email compromise (BEC), account takeovers, and multi-stage intrusions that span email, identity, endpoints, and networks.
Each of these attack classes operates at a different layer of the kill chain and exploits different weaknesses. Signature-based systems are effective at stopping known exploits, but struggle against novel or evasive threats. Sandboxes and ML-based file analysis improve zero-day detection, yet remain dependent on payload execution or observable exploitation stages. Endpoint and XDR platforms provide strong visibility post-compromise, but by design operate after an attacker has already established a foothold.
This is why modern security architectures must be multi-layered, combining multiple detection categories, each optimized for a specific class of threats and stage of attack. Understanding how these detection categories derive verdicts—and equally important, where they fail—is critical for designing defenses that reduce attacker dwell time, limit blast radius, and prevent threats before they reach users or endpoints.
In the sections that follow, I break down the major detection categories used across enterprise security today, explaining how they work, what classes of attacks they detect, and the tradeoffs inherent in each approach. The goal is not to promote any single technique, but to provide a clear, architectural framework for building security systems that are resilient, adaptive, and aligned with how modern attacks actually unfold.
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Category of Detection
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Focus / Description
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Class of Attacks Detected / Stopped
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Other Notes
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Signature Based
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Detects known threat patterns, hashes, or indicators of compromise (IOCs)
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Known malware, malicious domains/IPs, phishing with known IOCs, known vulnerabilities and CVE exploits.
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Highly scalable and efficient for preventing known exploits and repeatable attack patterns. Limited against novel or evasive threats.
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Sandbox / Behavioral Analysis
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Observes and analyzes the behavior of files, scripts, or web content in an isolated environment to detect malicious behavior
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Malware, phishing, fileless attacks, drive-by downloads
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Effective for zero-day threats. Can be bypassed if landing pages or exploitation stages are hidden using evasive techniques (delays, environment checks, conditional payloads /Landing Pages).
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ML/AI Applied to Files
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Uses machine learning or AI to predict maliciousness of files or HTML pages
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Malicious Files, executables, phishing pages
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Susceptible to evasion when critical features from landing pages or exploitation stages are hidden, obfuscated, or altered. New variations in exploitation stages can also reduce accuracy
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Intent-Based Threat Prevention™
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Analyzes the intent of communication and leverages contextual reasoning from auxiliary features to derive verdict.
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BEC, phishing, malicious attachments, malicious links, conversational payloads
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Requires access to communication content (e.g., email text) to derive intent. Non-reliance on landing pages or exploitation artifacts makes it resilient to evasions. BEC detection scales across impersonation of any role—executive, employee, customer, or vendor.
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Human Behavioral Analysis
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Detects anomalies in human behavior and communication patterns to derive verdict.
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Unusual email requests, impersonation, BEC, insider threats
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Requires personnel data for profiling. Relationship graphs and baselines take time to build, creating an initial window of opportunity for exploitation.
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EDR (Endpoint Detection & Response)
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Monitors endpoint activity and enables remediation
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Active compromise, in-memory attacks, persistence mechanisms, fileless attacks, malicious process injections, lateral movement
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Detection is post-execution. Attacker dwell time before remediation is a critical risk factor.
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XDR (Extended Detection & Response)
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Correlates data across multiple telemetry sources (email, endpoint, network) for holistic detection
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Malware, breaches, account compromise, privilege escalation, lateral movement, multi-stage attacks
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Improves visibility and correlation, but detection and response still occur after initial compromise, making dwell time critical.
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Deception / Honey Tokens
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Uses breadcrumbs, lure to trap attackers, malware to trigger alerts from deception services
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Lateral movement, insider threats, reconnaissance, credential abuse, targeted attacks
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Detection occurs after attacker interaction, so dwell time remains a key consideration.
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Conclusion
Each detection category targets specific threat classes and stages of the attack lifecycle, but no single approach is sufficient. Modern attacks are no longer linear or purely payload-driven—evasion is now built into the architecture of attack design.
While EDR and XDR provide valuable visibility and response, they typically act after initial compromise, leaving organizations exposed to dwell time and heightened business impact. The future of security lies in layered detection that shifts left—understanding intent, leveraging contextual reasoning, and proactively identifying threats before execution. Only this combination delivers robust, adaptive protection against today’s sophisticated adversaries.
