What is threat hunting?
Proactive threat hunting is a hypothesis-driven security practice that assumes attackers are already present in your environment. It uses human-led investigations and security telemetry to actively search for hidden threats that bypass automated detection tools.
Traditional security tools work like burglar alarms that go off when someone breaks in. Threat hunting works more like a detective who searches through evidence to find intruders who slipped past the alarm. These hidden attackers can stay in your systems for months, stealing data or preparing for bigger attacks. Attackers typically lurk for 11 days before getting caught – but teams that hunt proactively find them in just 10 days, while those waiting for external alerts take 26 days.
The process flips security from reactive to proactive. Instead of responding to alerts after damage occurs, you actively hunt for threats before they strike. This approach dramatically reduces "dwell time" –how long attackers can operate undetected in your environment.
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How threat hunting works in modern environments
Threat hunting follows a three-step cycle that repeats continuously. First, hunters create a hypothesis about potential threats based on intelligence or suspicious activity. Second, they investigate using security tools and data analysis. Third, they resolve findings by containing threats and improving defenses.
The process runs alongside your normal security operations. While automated tools handle known threats, hunters search for advanced persistent threats (APTs) and unknown attack methods. They look for subtle signs like unusual network traffic, strange user behavior, or suspicious file changes that automated systems might miss.
When hunters find real threats, they don't just remove them. They study the attacker's tactics, techniques, and procedures (TTPs) to strengthen future defenses and share intelligence with the broader security community.
Types of threat hunting methodologies
Security teams use different hunting approaches depending on available data and threat intelligence. Each method serves specific purposes and can be combined for comprehensive coverage.
Hypothesis-driven hunting starts with educated guesses about potential threats. Hunters might theorize that attackers are using a new technique they read about in threat reports. They then search for evidence supporting this theory across their environment.
Intelligence-based hunting uses known indicators of compromise (IoCs) and indicators of attack (IoAs) from threat feeds. IoCs are artifacts left after an attack occurs—like specific malware file hashes or IP addresses used by attackers. IoAs are behavioral patterns that suggest an attack is in progress—like unusual privilege escalation or suspicious lateral movement patterns
Analytics-driven hunting relies on machine learning and behavioral analysis to spot anomalies. This method finds unusual patterns that might indicate unknown threats, like a user account suddenly accessing sensitive data it never touched before.
Threat hunting techniques and approaches
Effective threat hunting requires structured approaches that ensure comprehensive coverage. Different techniques work better for different scenarios and threat types.
Structured hunting uses frameworks like MITRE ATT&CK to guide investigations. Pair ATT&CK mapping with a security graph to visualize attack paths and prioritize hunts on the most exploitable combinations of misconfigurations, identities, vulnerabilities, and data exposure. You might focus on specific techniques like credential dumping or lateral movement based on your environment's risks.
Unstructured hunting takes a more exploratory approach. When you discover a suspicious indicator, you follow the trail wherever it leads. This technique relies heavily on hunter experience and intuition to connect dots that automated tools miss.
Entity-focused hunting concentrates on your most critical assets—what security professionals call "crown jewels." You identify the most valuable data and systems, then hunt specifically for threats targeting those resources. This risk-based approach ensures you protect what matters most.
Situational hunting responds to specific events or intelligence. When a new vulnerability affects your technology stack, you immediately hunt for signs of exploitation. This reactive hunting complements your ongoing proactive efforts.
These approaches work together in mature hunting programs. Baseline establishment is critical – hunters first map normal behavior patterns for users, applications, and systems using weeks or months of historical data. This baseline enables detection of anomalies that might indicate compromise. A typical hunt might begin with intelligence-based searching for specific IoCs, then shift to hypothesis-driven investigation when suspicious activity emerges, followed by analytics-driven analysis to understand the full scope of compromise.
Why threat hunting is critical for cloud security
Cloud environments create unique challenges that make threat hunting essential. Resources like containers and serverless functions appear and disappear rapidly, creating blind spots where attackers can hide. Nearly half (47.1%) of cloud breaches start with weak credentials, and another 29.4% exploit misconfigurations—attack patterns that traditional security tools often miss.
Traditional security tools struggle with this dynamic environment. They're built for static networks where servers stay in one place with predictable behavior. Cloud workloads move, scale, and change too quickly for these older approaches to track effectively.
Cloud-specific risks make hunting even more important:
Misconfigured storage buckets that expose sensitive data
Overly permissive identity roles that give attackers excessive access
Exposed APIs that provide direct entry points
Container escapes: attacks where malicious code breaks out of container isolation to access the underlying host system, potentially compromising other containers or the entire cluster
The shared responsibility model adds complexity: cloud providers secure the physical infrastructure, hypervisors, and foundational services, while customers are responsible for securing their operating systems, applications, data, network configurations, and identity management. This division means threats can exploit vulnerabilities in either layer, requiring hunters to understand both cloud provider security controls and their own implementation gaps.
Cloud-native logging presents unique challenges for hunters. Ephemeral resources like containers and serverless functions may exist for only minutes, making it difficult to collect sufficient telemetry. The massive scale of cloud environments generates enormous log volumes that can overwhelm traditional analysis tools. Hunters must implement streaming analytics and focus on high-value signals rather than attempting to analyze every event.
Threat hunting tools and technologies
Effective threat hunting requires a layered technology stack that moves from raw data collection to detection, analytics, and enrichment. Modern platforms increasingly embed AI/ML at each stage—helping surface hidden threats, tune detection logic, and reduce analyst workload.
Foundational visibility
Every hunt begins with comprehensive visibility. Security Information and Event Management (SIEM) platforms provide the backbone by centralizing logs across the environment. In the cloud, this means ensuring coverage of AWS CloudTrail (including data events), VPC Flow Logs, Amazon EKS audit logs; Azure Activity Logs, Azure AD sign-in/audit logs, NSG flow logs, AKS audit logs; and GCP Audit Logs (Admin, Data Access), VPC Flow Logs, and GKE audit logs—alongside identity provider logs and Kubernetes control plane telemetry. Both control plane (management actions) and data plane (resource activity) logs are essential for capturing the full scope of attacker behavior.
Data retention requirements typically range from 90-365 days for active hunting, with longer-term storage for compliance needs. Establishing behavioral baselines requires weeks or months of historical data to distinguish normal patterns from anomalous activity.
Detection and response
Detection and response platforms extend visibility beyond logs to monitor actual system behavior. Endpoint Detection and Response (EDR) tools track processes, file changes, and network connections on individual devices. Extended Detection and Response (XDR) platforms correlate signals across endpoints, networks, email, and cloud services to identify attack patterns that span multiple systems. Cloud-focused detection platforms add specialized capabilities for dynamic workloads, containers, and serverless functions that traditional tools struggle to monitor.
AI increasingly augments these detection capabilities by generating or refining detection logic, correlating weak signals that individually seem benign but collectively indicate compromise, and spotting malicious activity that evades traditional signature- or rule-based approaches. These detections feed into analytics and automation layers that help scale investigations across the entire environment.
Analytics and enrichment
Once the data is collected, analytics platforms apply AI and machine learning models to detect anomalies at scale, reduce false positives, and highlight behaviors that don't match established baselines. This is where subtle attacker patterns emerge—like privilege escalation attempts or unusual lateral movement. Some platforms use AI to automate detection engineering itself, proposing new rules or queries for human hunters to validate, effectively turning past hunts into reusable detections.
Automation and orchestration
Security orchestration, automation, and response (SOAR) platforms streamline repetitive hunting tasks and incident response workflows. These tools can automatically gather contextual information, execute standard investigation procedures, and propose response actions based on predefined playbooks.
Organizations like Grammarly have demonstrated the potential impact, using AI-powered automation to reduce investigation time from 30-45 minutes to under 4 minutes for routine incident triage—a nearly 90% efficiency improvement. Modern automation follows a "human-in-the-loop" approach where AI handles data collection and initial analysis while human experts make final decisions and guide strategic investigations.
Threat intelligence integration
Threat intelligence feeds add external context by supplying up-to-date indicators of compromise (IoCs – artifacts left after attacks), indicators of attack (IoAs – behavioral patterns suggesting ongoing attacks), and adversary tactics, techniques, and procedures (TTPs). When combined with AI-driven analytics and detection, these feeds help prioritize investigations by linking emerging attacker behaviors with internal telemetry and focusing efforts on the most relevant threats to your specific environment.
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Implementing effective threat hunting programs
Building a successful hunting program requires more than just tools—it needs the right people, processes, and data foundation. Effective programs balance automation with human expertise to maximize threat detection capabilities.
Your hunting team needs analysts who understand both technology and attacker psychology. These professionals must know how networks operate, how attackers think, and how to analyze complex data patterns. They need curiosity, persistence, and strong analytical skills.
Data collection forms the foundation of effective hunting. You need comprehensive visibility across endpoints, networks, cloud services, and identity systems. The more data you collect, the better hunters can understand normal behavior and spot anomalies.
Key program components include:
24/7 operations to catch threats that operate outside business hours
Threat intelligence integration to stay current with evolving attack methods
Automation tools to handle routine analysis and free hunters for complex investigations
Collaboration processes to share findings across security teams. Use shared investigation timelines and graph-based views so SecOps, CloudSec, and engineering can align quickly on root cause and owners.
Governance alignment: Map hunting activities to controls in common frameworks (e.g., ISO/IEC 27001:2022 Annex A such as A.5.7 Threat intelligence and A.8.16 Monitoring activities; SOC 2 CC7 for monitoring and incident response).
How Wiz Defend transforms cloud threat hunting
Traditional threat hunting faces significant challenges in cloud environments. Alert fatigue overwhelms analysts, blind spots hide threats, and manual correlation takes too much time. Wiz Defend addresses these problems with a unified platform that automates investigation and provides deep cloud context.
Wiz’s agentless coverage removes blind spots across multi-cloud and K8s without performance hit. The Wiz Security Graph connects detections to identities, configurations, and data to surface real attack paths and the fastest fix.
A lightweight eBPF-based sensor (Extended Berkeley Packet Filter, a Linux kernel technology that safely runs custom programs without kernel modules) adds real-time runtime signals
The platform transforms hunting from a manual, time-intensive process into an efficient, automated workflow. Teams using advanced automation cut breach costs to $4.44 million and slash detection time to 241 days—the fastest response times we've seen in nine years.
Wiz Defend's key advantages include:
High-fidelity detections that minimize false positives through curated threat intelligence
Automated correlation that connects security events across your entire cloud environment
Root-cause remediation that traces threats back to vulnerable code for permanent fixes
Comprehensive coverage across cloud services, containers, and serverless functions
The platform transforms hunting from a manual, time-intensive process into an efficient, automated workflow. Security teams can focus on strategic threat analysis instead of data collection and correlation.
See how Wiz Defend levels up your cloud threat hunting—with agentless coverage, graph-powered context, and automated investigations that cut noise and speed response.
Detect active cloud threats
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