What is threat hunting?

What is threat hunting?

Threat hunting is the proactive practice of searching for signs of malicious activity or hidden threats within an organization's networks, systems, and endpoints that have evaded traditional cybersecurity measures. Unlike automated detection systems that wait for alerts, threat hunting involves security analysts actively pursuing sophisticated threats through hypothesis-driven investigations, behavioral analysis, and threat intelligence correlation.

With 79% of modern cyberattack detections now being malware-free according to CrowdStrike's 2025 Global Threat Report, and organizations with dedicated threat hunting teams reducing attacker dwell time by 50%, proactive threat hunting has become essential for modern cybersecurity defense.

What is threat hunting in cybersecurity?

Threat hunting represents a fundamental shift from reactive to proactive cybersecurity. Instead of waiting for alerts, security analysts actively search for hidden threats using hypotheses, behavioral analysis, and threat intelligence correlation. This proactive approach is crucial because sophisticated attackers often evade traditional signature-based detection systems.

Threat hunting is made powerful by its multidimensional approach:

• Human intuition: Skilled analysts think like attackers, anticipating their tactics, techniques, and procedures (TTPs).
• Structured frameworks: Hunts are guided by models like the ATT&CK framework, providing a shared language for mapping adversary behavior.
• Advanced analytics: Correlating logs, telemetry, and threat intelligence gives every hunt context, depth, and precision, helping analysts distinguish real threats from background noise.

Threats in cybersecurity and what hunters find

What is a cyberthreat?

In cybersecurity, a threat is any potential danger that could exploit a vulnerability to cause harm, whether through data theft, service disruption, or unauthorized access. A threat doesn’t have to be an actual attack, just the possibility of harm.

Types of threats:

• External threats: Hackers, cybercriminals, malware, ransomware, and nation-state actors.
• Internal threats: Malicious insiders, negligent employees, or compromised accounts.
• Environmental threats: System failures, power outages, or natural disasters that may compromise security indirectly.
• Key insight: Threat hunters focus primarily on external and internal threats, as these require active investigation to uncover sophisticated attack patterns and behavioral anomalies.

What does cyberthreat hunting detect?

Threat hunters focus on advanced threats that often evade automated defenses and signature-based tools. These threats are typically stealthy, persistent, and designed to blend into normal system activity.

They include:

• APTs: Sophisticated, targeted intrusions designed to remain hidden while extracting sensitive data over extended periods.
• Insider threats: Malicious or compromised insiders abusing legitimate access for data theft, sabotage, or privilege escalation.
• Credential-based attacks: Brute-force attempts, pass-the-hash attacks, or unauthorized privilege escalations exploiting weak or stolen credentials.
• Lateral movement: Adversaries pivoting between systems to expand their foothold, often using remote execution methods or stolen credentials.
• Living-off-the-land attacks: Attackers misusing trusted tools like PowerShell, WMI, or PsExec to hide malicious actions within normal activity. Over 84% of high-severity attacks rely on these stealthy techniques, according to DarkReading.
• Cloud-focused attacks: Unauthorized access to SaaS platforms, misconfigured cloud services, or compromised federated identities.
• Zero-day exploits: Early-stage exploit activity targeting unknown vulnerabilities before patches or signatures are available.
• Evasive malware and ransomware: Advanced strains designed to evade antivirus and endpoint protections while staging data exfiltration or encryption.

These threats often leave behind subtle indicators, such as behavioral anomalies or unusual system patterns that hunters are trained to detect.

Threat hunting methodologies and models

Core threat hunting methodologies

Threat hunting isn’t a one-size-fits-all process. Depending on the objective, data sources, and investigative approach, threat hunting typically takes one of these three forms:

• Structured hunting: A methodical, framework-guided approach using models like the ATT&CK framework.
  • Goal: Search for defined indicators of attack (IoAs) and map adversary TTPs to known frameworks.
  • Use case: Targeted investigations into known threat actor behaviors or techniques.
  • For example: Detecting suspicious PowerShell activity.
    • Hypothesis: Attackers may attempt lateral movement using encoded PowerShell commands.
    • Data sources: Windows event logs (Event ID 4104), endpoint telemetry, and threat intel feeds.
    • Tools: Log360 with ATT&CK mapping and UEBA.
    • Process: Query logs for suspicious flags, correlate with user activity, and investigate unusual admin logins.
    • Success metrics: Detection rate, time to detect (TTD), and reduction in attacker dwell time.
• Unstructured hunting: A reactive investigation, often triggered by the discovery of an anomaly or a new IoC in the environment.
  • Goal: Trace the source of the IoC, determine the attack scope, and assess if the threat is still active.
  • Use case: When you need to validate or investigate anomalies quickly with limited context.
  • For example: Investigating suspicious outbound connections.
    • Trigger: SOC observes traffic to a known malicious IP.
    • Hypothesis: The connection may indicate data exfiltration or C2 activity.
    • Data sources: Firewall logs, DNS logs, and NetFlow data.
    • Tools: Log360 for traffic log analysis and correlation.
    • Process: Isolate traffic to the suspicious IP, identify the host, and review related DNS queries.
    • Success metrics: Speed of root cause identification, percentage of confirmed threats vs. false positives.
• Situational or entity-driven hunting: Hunts driven by an organization’s unique context, such as insights from risk assessments or findings from a vulnerability analysis.
  • Goal: Identify and investigate threats that could compromise critical assets or high-value entities (e.g., domain controllers, finance systems, executive accounts).
  • When to use: When you need to assess risks to specific systems or prioritize investigations based on business-critical assets.
  • For example: Protecting domain controllers from privilege escalation.
    • Context: Risk assessment identifies domain controllers as high-value targets.
    • Hypothesis: Attackers may attempt to escalate privileges by abusing admin accounts.
    • Data sources: Windows security logs (4624, 4672), AD audit trails, and Kerberos tickets.
    • Tools: Log360 with AD audit reports and UEBA.
    • Process: Monitor abnormal logins, detect unusual Kerberos ticket requests, and correlate access patterns.
    • Success metrics: Number of suspicious admin activities detected, reduction in false positives, and mean time to investigate.

All these hunts start with a hypothesis, which is drawn from observations, security telemetry, or external triggers. This hypothesis becomes the springboard for deeper investigations into potential threats.

Threat hunting models

Threat hunting isn’t random exploration; it’s a methodical process, blending frameworks like ATT&CK with analytics, intel, and human intuition.

Threat hunting generally follows one (or a combination) of these models:

• Intel-driven hunting: Guided by known IoCs, threat actor profiles, and external intelligence, this model allows analysts to search for signs of campaigns active in the wild.
• Hypothesis-driven hunting: Analysts form investigative questions (e.g., Is an adversary using PowerShell for lateral movement?) and explore telemetry for supporting evidence, using frameworks like ATT&CK to structure the hunt.
• Anomaly-driven hunting: Leveraging UEBA to detect subtle deviations from established baselines, such as sudden spikes in service account activity.

The threat hunting process step by step

Threat hunting begins not with a flood of alerts but with a trigger, or a clue that something might be amiss. This can come from:

• A suspicious anomaly: Unusual login times, privilege escalations, or unexpected process behavior.
• A lead from threat intelligence: IoCs or known attacker TTPs mapped to frameworks like ATT&CK.
• An analyst’s hypothesis: Intuition-driven questioning, such as: Are attackers using PowerShell for lateral movement in our environment?

The five-stage threat hunting life cycle

Threat hunting is an iterative process designed to uncover stealthy threats proactively. It blends human intuition, attacker knowledge (e.g., ATT&CK® TTPs), and advanced analytics to stay ahead of adversaries.

This life cycle comprises five key stages:

• Stage 1: Preparation: Build strong hypotheses based on threat intelligence, recent incidents, or suspected adversary behavior. Configure dashboards and align hunts to frameworks like ATT&CK.
• Stage 2: Data collection and analysis: Aggregate and normalize telemetry from on-premises, cloud, and endpoints. Enrich data with behavioral context and threat intel for meaningful analysis.
• Stage 3: Investigation: Correlate anomalies, threat indicators, and detections across sources. Apply filters to focus on high-value entities and use analytics to confirm or refute hypotheses.
• Stage 4: Resolution and reporting: Contain threats through response workflows or playbooks. Generate framework-aligned reports to communicate findings to stakeholders and compliance teams.
• Stage 5: Feedback loop: Refine detection rules, update behavioral baselines, and integrate lessons learned into new hunts, turning findings into a feedback loop for stronger defenses.

Pro tip: Use Log360's ATT&CK-aligned dashboards to pivot from a suspicious event directly into hunting related TTPs across your environment.

Threat hunting tools and technologies

Effective threat hunting requires a multi-layered tool set that blends visibility, analytics, threat intelligence, and automation. These technologies enable hunters to form hypotheses, investigate anomalies, and respond quickly to emerging threats.

Essential threat hunting tools:

• Security information and event management (SIEM): Platforms like Log360 aggregate and analyze security logs from on-premises, cloud, and remote systems, providing a centralized view of activity.
• UEBA: Detects behavioral anomalies by establishing baselines for users, devices, and entities, flagging deviations that may indicate insider threats or compromised accounts.
• Endpoint and extended detection and response (EDR/XDR): Provides endpoint-level visibility and expands detection across network, cloud, and application layers for holistic investigations.
• Managed detection and response (MDR): Offers outsourced, 24/7 threat hunting and response using expert-driven investigations and continuous monitoring, ideal for organizations lacking specialized in-house skills.
• Threat intelligence platforms: Ingest and operationalize IoCs, attacker TTPs, and cyberattack trends using standards like STIX and TAXII.
• Forensic search and analytics: Enables high-speed querying of vast datasets to validate hypotheses and uncover attack paths.
• Detection engineering: Connects disparate events across logs and telemetry to identify multi-stage attacks.
• Security orchestration, automation, and response (SOAR): Integrates playbooks and orchestration tools to contain threats or escalate incidents without manual intervention.

How these tools work together

These tools often work in tandem. For example, combining ATT&CK-aligned detection rules, UEBA-driven anomaly alerts, and threat intel feeds within a SIEM platform like Log360 enables hunters to pinpoint threats with precision and context.

Threat hunting vs. related disciplines

Threat hunting works alongside several other core security practices, and each serves a distinct purpose. Understanding how they relate helps organizations build a stronger, more integrated security strategy.

Effective threat hunting does not replace established security practices. It complements them to create a more resilient defense against evolving cyber risks.

Benefits of proactive threat hunting

Proactive threat hunting gives security teams the upper hand, identifying and mitigating threats that slip past automated defenses.

Key benefits:

• Early detection of stealthy threats: Identify APTs, insider misuse, and other low-and-slow attacks before they escalate.
  • Powered by: ATT&CK-mapped rules and cloud-delivered threat intelligence.
• Reduced attacker dwell time: Minimize the window of attacker activity, limiting the opportunity for lateral movement, privilege escalation, or data exfiltration.
  • Powered by: UEBA-driven anomaly detection and real-time correlation.
• Enhanced SOC efficiency: Prioritize high-value alerts and reduce analyst fatigue by focusing on real threats instead of noisy detections.
  • Powered by: Object-level filtering that zeroes in on critical assets.
• Informed detection tuning: Feed insights back into SIEM rules, UEBA baselines, and incident response playbooks, making your detection framework smarter over time.
  • Powered by: Rule tuning insights and customizable detection packs.
• Better alignment with business risks: Focus hunts on critical assets, systems, and high-value entities, aligning cybersecurity efforts with organizational priorities.
  • Powered by: Cloud-delivered threat intel feeds mapped to the ATT&CK framework.

In practice: By continuously iterating on findings, proactive hunts transform detection from reactive alerting to an intelligence-driven defense strategy, reducing exposure, improving resilience, and strengthening your overall security posture.

Threat hunting challenges and solutions

While threat hunting strengthens an organization’s security posture, it comes with significant operational challenges.

Common threat hunting challenges

• Alert fatigue: Security teams are often overwhelmed by high volumes of noisy alerts, making it difficult to distinguish real threats from false positives.
• Data sprawl: Hybrid IT environments spanning on-premises, cloud, and remote endpoints generate vast, fragmented datasets that are difficult to consolidate and analyze.
• Skill gaps: Effective threat hunting demands specialized expertise in attacker TTPs, advanced analytics, and complex query languages—skills that many SOCs struggle to maintain.
• Manual rule tuning: Static or outdated detection rules require frequent manual updates to keep pace with evolving threats, consuming valuable analyst time.
• Scaling limitations: As log volumes grow and investigations become more complex, many security tools struggle to maintain performance, speed, and reliability.

How Log360 addresses these challenges

• For alert fatigue: Object-level filtering to prioritize high-risk assets and cut false positives at the source.
• For data sprawl: Unified log ingestion and contextual enrichment in one scalable console.
• For skill gaps: No-code filtering and analyst-friendly dashboards that make hunting intuitive for all skill levels.
• For manual rule tuning: Cloud-delivered detection content with over 2,000 rules, auto-updated for evolving threats.
• For scaling limitations: Horizontally scalable architecture with high-availability for continuous, fast hunting.

Threat hunting with Log360: A practical guide

Threat hunting isn’t just about spotting anomalies, it’s about transforming raw data into actionable intelligence. ManageEngine Log360 elevates this practice by delivering enterprise-grade detection engineering, context-rich insights, and a scalable architecture, empowering SOC teams to hunt with speed, precision, and confidence.

Five-phase implementation guide

Phase 1: Preparation

• Leverage prebuilt content: Start with over 2,000 cloud-delivered detection rules mapped to ATT&CK tactics and techniques.
• Use threat intelligence: Integrate STIX/TAXII-compatible threat feeds to stay ahead of evolving attacker TTPs.
• Configure dashboards: Set up custom views for critical entities, attack stages, or high-risk assets to focus your hunts where it matters most.

Phase 2: Data collection and analysis

• Choose your approach: Run hypothesis-driven, intel-driven, or anomaly-driven hunts using Log360’s flexible rule framework.
• Apply object-level filters: Narrow detections to privileged accounts, high-value OUs, or sensitive assets to cut through noise.
• Surface anomalies: Utilize UEBA-driven behavior baselines to identify deviations that may indicate stealthy attacker activity.

Phase 3: Investigation

• Centralized console: Use a single-pane interface to correlate ATT&CK-based detections, threat intelligence, anomalies, and custom rules.
• AI-powered Zia Insights: Automatically summarize incidents, identify relationships between users, systems, and attack stages, and prioritize high-risk leads for deeper analysis.
• Forensic search: Conduct fast, contextual searches across normalized logs for deeper investigative dives.

Phase 4: Resolution and reporting

• Trigger workflows: Use prebuilt response playbooks or integrate with SOAR tools to accelerate containment.
• Generate reports: Create ATT&CK-mapped hunting reports for compliance, leadership, or post-incident reviews.
• Collaborate effectively: Share findings with IT, incident response, or management teams for coordinated action.

Phase 5: Continuous improvement

• Tune detections: Use rule performance insights to reduce noise, eliminate redundancies, and improve accuracy.
• Refine baselines: Continuously update anomaly detection models as organizational behavior evolves.
• Use findings: Incorporate lessons learned into new rules, dashboards, and playbooks for stronger future hunts.

From building smarter hypotheses to automating responses and refining detection strategies, Log360 delivers the depth, context, and scalability needed to uncover threats others miss, making every phase of your hunting life cycle sharper and more impactful.

Frequently asked questions