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Get Free TrialMore about Spectra Assure Free TrialThreat hunting is a proactive approach in cybersecurity where skilled analysts search for hidden threats within an organization’s network that may not be detected by automated security tools. Unlike traditional detection methods that rely on predefined signatures and alerts, threat hunting involves a more hands-on, investigative approach to identifying sophisticated threats, including zero-day vulnerabilities, advanced persistent threats (APTs), and malicious insiders.
The SANS institute’s 2020 threat hunting survey showed that 65% of organizations have threat hunting programs and that 29% of organizations plan to implement threat hunting practices over the next year.1
Many companies support effective malware analysis programs with threat hunting programs, ensuring that they consistently identify active and benign threats across their environment.
For example, in 2020, SolarWinds used ReversingLabs threat hunting tools. It located the active threats behind the supply chain attack that had targeted it and that had impacted 18,000 organizations and generated $40 million in damages.2
With malware analysis tools, this attack would have remained undetected for the foreseeable future, targeting more users and causing more damages to victims’ systems.
To maximize the effectiveness of a threat hunting program, organizations should adhere to several best practices related to methodologies, tools, team composition, and the integration of threat intelligence.
Three popular threat hunting methodologies include the hypothesis-driven approach, the review of attack indicators, and the use of ML and AI-based tools.
This includes spotting abnormalities, formulating an educated guess about what caused them, and reviewing data to validate whether potential threats should be investigated. Analysts use logs, endpoint telemetry, and network activity to confirm or disprove the hypothesis. The process is iterative and often leads to additional findings or new investigative paths.
Threat intelligence platforms are used to find indicators of compromise or attack that should then be researched further. Common indicators include malicious IP addresses, domains, file hashes, and suspicious behavioral patterns. Hunters compare these indicators against activity in their environment to identify potential threats and prioritize investigations.
With machine learning and artificial intelligence, data is automatically reviewed and abnormal behaviors can be identified.
Threat hunting tools complement existing endpoint detection and response (EDR) tools by offering more complex and detailed analysis to discover suspicious behaviors and patterns that EDR tools cannot detect.
Intelligence-driven hunting uses external threat intelligence as the starting point for an investigation. Rather than beginning with an analyst's hypothesis, hunters operationalize intelligence from:
Intelligence-driven hunting prioritizes behavioral TTPs over static indicators, since threat actors frequently rotate infrastructure. For instance, searching for evidence of PowerShell-based credential dumping or LOLBin (living-off-the-land binary) abuse identifies attacker behavior even when specific IOCs have changed.
While external feeds provide the 'what' and 'who,' effective hunts also rely heavily on internal intelligence. Insights from an organization's previous incidents are a critical piece of intelligence. Additionally, internal intelligence provides a baseline 'normal' behavior which is crucial in detecting anomalies.
Threat hunting tools go beyond traditional endpoint detection and response (EDR) solutions by providing deeper analysis of suspicious behaviors and patterns that automated tools might miss. These tools help hunters detect advanced threats that evade conventional defenses.
A specialized team focused solely on threat hunting is essential. These teams require a unique skill set, including expertise in using advanced tools, understanding the latest threat landscapes, and creatively thinking about potential attack vectors. Dedicated threat hunters can operate with a proactive mindset, continuously searching for threats rather than waiting for alerts.
Threat intelligence is crucial for informing and guiding threat hunting efforts. It provides context on the severity of threats, relevant IOCs, and the tactics, techniques, and procedures (TTPs) used by adversaries. By integrating threat intelligence, hunters can prioritize their efforts and focus on the most significant risks to their organization.
Threat detection and threat hunting are complementary but distinct practices.
Threat detection is reactive and automated: security tools analyze incoming data, match against known signatures and rules, and generate alerts when a match is found. It is fast and scalable — but it only catches what it already knows to look for.
Threat hunting is proactive and human-led: analysts actively search for threats that have not triggered any alerts, using hypotheses, behavioral analysis, and threat intelligence to uncover adversaries who have evaded automated defenses.
The most effective security programs run both in parallel. Detection tools handle known threats at scale; threat hunters focus on the unknowns the stealthy, dwell-time-extending intrusions that automated systems miss.
Key differences at a glance:
The threat hunting process follows a structured cycle:
Threat hunters rely on a layered toolset spanning collection, aggregation, analysis, and intelligence:
Effective threat hunting programs generate a continuous improvement loop - every hunt makes future defenses stronger.
When hunters identify suspicious behaviors that lacked prior detection coverage, those findings go directly to detection engineering. New SIEM correlation rules, EDR behavioral signatures, and custom YARA rules are created, tested, and deployed - closing gaps that allowed threats to evade existing controls.
Hunts also surface telemetry gaps: logs that are not being collected, sensors that are not deployed, or cloud services without audit trails enabled. Addressing these strengthens the raw material available for future hunts.
Over time, the cycle becomes self-reinforcing: richer telemetry enables more precise hypotheses, which produce better detections, which free hunter time for more sophisticated investigations. Threat hunting matures from a periodic exercise into a core driver of detection strategy.
Threat hunters rely on a layered toolset spanning collection, aggregation, analysis, and intelligence:
Building an effective in-house threat hunting program requires skilled Tier 3 SOC analysts, comprehensive tooling, and 24/7 operational capacity resources most organizations struggle to maintain.
The cybersecurity industry faces a significant skills shortage in experienced threat hunters. Seasoned analysts with the depth to identify sophisticated adversary behavior don't come cheap, and the learning curve for building that expertise internally is steep.
Managed threat hunting services address this gap by providing:
For organizations assessing their options, the right approach depends on existing team maturity, available tooling, and risk tolerance.
Many threat hunting programs structure their work using the MITRE ATT&CK® framework - a publicly available knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world attack observations.
ATT&CK provides hunters with a common language for describing adversary behavior: from initial access and execution through persistence, lateral movement, and exfiltration. Mapping hunt hypotheses to ATT&CK tactics helps teams:
For example, a hypothesis targeting credential misuse maps to ATT&CK Tactic TA0006 (Credential Access), enabling the hunter to reference specific techniques like T1003 (OS Credential Dumping) when building queries and documenting results.

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