What is enrichment in threat intelligence?
Enrichment in threat intelligence is the process of adding context, metadata, and relationships to raw security data to make it actionable. This means taking isolated data points like IP addresses, domain names, or file hashes and layering them with meaningful information about their origin, behavior, and potential threat level.
Think of it like this: a raw IP address tells you almost nothing. But when you enrich that IP address with its geolocation, reputation score, associated malicious campaigns, and historical activity, you suddenly have a complete picture that helps you decide if it's a real threat or just background noise.
The difference between raw threat data and enriched intelligence is like the difference between seeing a license plate number and knowing who owns the car, where it's been, and whether it's stolen. Enrichment transforms observables and Indicators of Compromise (IOCs) into comprehensive intelligence with attribution, Tactics, Techniques, and Procedures (TTPs), and risk scoring that security teams can actually use.
Cloud Attack Retrospective
In this report, we examine how threat actors target cloud environments and provide practical guidance on how Wiz helps detect and mitigate these threats.
Why threat intelligence enrichment matters for modern security operations
The volume and velocity of threats in cloud environments make manual analysis impossible. Ephemeral infrastructure, microservices, and multi-cloud deployments create blind spots that traditional security tools can't handle without proper enrichment.
Your security team can't manually investigate every alert at enterprise scale across multiple cloud security tools. Organizations typically face thousands of security events daily, making manual triage impossible without automated enrichment. Enrichment automates the initial investigation process, dramatically reducing your mean time to detect and mean time to respond. It also enables proactive threat hunting by connecting seemingly unrelated events across the MITRE ATT&CK framework, revealing multi-stage attack patterns.
In cloud-native architectures, understanding lateral movement paths and blast radius is critical. Enrichment provides the context you need to map these connections before attackers can exploit them.
Organizations like PROS have reduced their detection and response time by enriching alerts with contextual information about their multi-cloud environment, turning hours of investigation into minutes of focused action.
Core types of threat intelligence enrichment
Different types of enrichment add unique layers of context to help you understand the nature and potential impact of security events.
Graph-based correlation helps connect identities, configurations, vulnerabilities, and network paths to reveal toxic combinations and real attack paths. By modeling your cloud environment as an interconnected graph, enrichment can trace how an attacker might move from an initial compromise through privilege escalation to sensitive data access.
Reputation and threat actor enrichment
Reputation enrichment assigns risk scores to indicators based on historical observations and threat feeds. This includes threat reputation scoring that tells you if an IP address, domain, or file hash has been previously associated with malicious activity.
Attribution mapping links activity to suspected APT groups and campaigns based on TTP overlap, infrastructure patterns, and historical indicators, providing probabilistic confidence scores. When you see an indicator that's been used by a specific threat actor, you immediately understand their motives, sophistication level, and typical attack patterns.
Infrastructure and network enrichment
Infrastructure enrichment adds network context including Autonomous System Numbers (ASN), hosting providers, DNS history, and certificate details. This helps you understand the relationships between different pieces of attacker infrastructure.
Passive DNS data shows you historical domain-to-IP mappings, while WHOIS data reveals domain registration information. SSL certificate analysis can expose connections between different malicious domains that might not be obvious otherwise.
Behavioral and temporal enrichment
Behavioral enrichment captures patterns over time including first seen and last seen timestamps, activity frequency, and behavioral anomalies. This temporal context helps you distinguish between current threats and historical artifacts.
Understanding when an indicator first appeared and how frequently it's been observed helps with vulnerability prioritization and cloud threat hunting efforts.
Code-to-Cloud Enrichment
A key differentiator for cloud security enrichment is connecting a runtime security event (like a vulnerability or a misconfiguration) back to the original source code, Infrastructure-as-Code (IaC) template, or developer who introduced it.
This is the code-to-cloud link. By enriching a cloud resource ID (e.g., an S3 bucket ARN) with its originating Git repository and commit ID, security teams can facilitate a shift left by providing developers with actionable, code-based fixes, rather than just post-production console remediation.
Cloud-specific enrichment
Cloud environments require unique enrichment that traditional tools can't provide, revealing multi-dimensional risks through contextual correlation:
Toxic combination examples:
Exposed EKS service + overprivileged service account + reachable S3 with PII: An internet-facing Kubernetes service running with a service account that has s3:* permissions to a bucket containing customer data creates a direct path from external access to data exfiltration
Vulnerable container image + privileged pod + host network access: A container with CVE-2024-1234 running in privileged mode with host network access enables container escape and lateral movement to the underlying node
Misconfigured security group + unpatched EC2 + IAM role with admin rights: An EC2 instance with SSH open to 0.0.0.0/0, running outdated software, and attached to an IAM role with AdministratorAccess creates a critical entry point
Multi-cloud normalization challenges:
Cloud-specific enrichment must normalize resource models across AWS, Azure, and GCP:
AWS IAM roles ↔ Azure Managed Identities ↔ GCP Service Accounts
AWS Security Groups ↔ Azure Network Security Groups ↔ GCP Firewall Rules
AWS S3 ↔ Azure Blob Storage ↔ GCP Cloud Storage
Unified resource models enable consistent policy enforcement and detection logic across all cloud providers, eliminating blind spots that arise from platform-specific tools.
Expected enrichment outcomes:
Effective cloud enrichment produces actionable insights like "Internet-exposed RDS database with default credentials and network path to production VPC" rather than separate alerts for "RDS misconfiguration" and "overly permissive security group." This contextual correlation reduces alert volume by 70-80% while surfacing critical attack paths that individual findings would miss. This type of enrichment is essential for Cloud Infrastructure Entitlement Management (CIEM) and Data Security Posture Management (DSPM).
How threat intelligence enrichment works in practice
The enrichment pipeline transforms raw data into prioritized, actionable intelligence through several automated steps. It starts with data collection from various security tools, then normalizes the data into a standard format for consistency.
Agentless cloud integrations reduce deployment friction and help organizations reach high-fidelity enrichment quickly, without adding workload overhead or requiring agent installation across thousands of resources. This agentless scanning approach is particularly valuable in dynamic cloud environments where ephemeral workloads make agent-based monitoring impractical. A correlation engine queries internal and external threat intelligence sources to append context to each observable.
A correlation engine queries internal and external threat intelligence sources to append context to each observable. This can happen at different integration points—during ingestion for immediate context, during analysis for ongoing investigations, or at alert generation for better triage.
Global enterprises achieve broad visibility across multi-cloud environments by implementing automated enrichment workflows that correlate security data from thousands of assets into a unified view, reducing mean time to investigate (MTTI) by 60-80%.
Machine learning plays a crucial role in identifying patterns and relationships that human analysts might miss. AI-driven triage and prioritization can reduce mean time to detect (MTTD) by 40-60% and mean time to respond (MTTR) by 50-70% when combined with high-quality enrichment data. Graph-based analysis using a security graph reveals hidden connections between disparate indicators, enabling automated attack path analysis that surfaces complex threats.
Common data sources for threat intelligence enrichment
Effective enrichment pulls context from multiple authoritative sources, each adding specific layers of intelligence:
Cloud-native logs and telemetry:
AWS CloudTrail, Azure Activity Logs, and GCP Audit Logs provide identity and API activity context
VPC Flow Logs and network telemetry reveal communication patterns and lateral movement paths
Kubernetes audit logs expose container orchestration events and pod-to-pod relationships
Vulnerability and exploit intelligence:
National Vulnerability Database (NVD) provides CVE details and CVSS scores
CISA Known Exploited Vulnerabilities (KEV) catalog identifies actively exploited flaws
Exploit databases like Exploit-DB show proof-of-concept availability
Infrastructure and network intelligence:
Passive DNS services reveal historical domain-to-IP mappings and infrastructure pivots
WHOIS databases provide domain registration and ownership details
Certificate Transparency logs expose SSL/TLS certificate relationships
ASN and ISP data identify hosting providers and network ownership
Threat actor and campaign intelligence:
STIX/TAXII feeds from ISACs and commercial providers deliver structured threat data
Malware sandbox reports from services like VirusTotal or Any.Run show file behavior
Dark web monitoring surfaces credential leaks and threat actor communications
Identity and access context:
SSO and IdP logs from Okta, Azure AD, or AWS IAM reveal authentication and authorization patterns
Privileged access management (PAM) systems track high-risk credential usage
EDR and runtime telemetry show process execution and lateral movement attempts
Each source maps to specific enrichment use cases: CloudTrail events combined with IAM policies reveal privilege escalation paths, KEV data prioritizes exploitable vulnerabilities, and Passive DNS enables infrastructure pivoting during threat hunts.
Architecture patterns for threat intelligence enrichment
Enrichment architectures vary based on latency requirements, data volume, and use case priorities:
In-line vs. out-of-band enrichment:
In-line enrichment occurs in the detection pipeline before alerts reach analysts, adding context in real-time. Out-of-band enrichment happens asynchronously, enriching stored events for historical analysis and threat hunting. High-severity alerts typically trigger in-line enrichment, while bulk telemetry uses out-of-band processing to avoid latency.
Batch vs. streaming enrichment:
Batch enrichment processes events in scheduled intervals (hourly, daily), suitable for vulnerability scans and compliance reporting. Streaming enrichment processes events as they arrive, essential for runtime threat detection and incident response. Modern cloud environments typically combine both: streaming for active threats, batch for posture management.
Progressive enrichment tiers:
Not every event needs deep enrichment. Implement tiered strategies:
Tier 1 (Lightweight): Basic tagging at ingestion—geolocation, reputation scores, asset classification—applied to all events
Tier 2 (Contextual): Graph correlation and relationship mapping for medium-severity alerts
Tier 3 (Deep): Full forensic enrichment with historical analysis, TTP mapping, and attack path reconstruction for high-severity incidents
Integration points across the security workflow:
Ingestion-time enrichment: Tag events with basic context (asset owner, environment, criticality) as they enter your data lake
Analysis-time enrichment: Correlate events during threat hunts and investigations, joining cloud logs with vulnerability data
Alert-time enrichment: Stitch together complete attack narratives when generating SOC alerts, including blast radius and remediation owners
This layered approach balances enrichment depth with performance, ensuring critical threats get immediate deep context while routine events receive efficient lightweight tagging.
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Benefits of automated threat intelligence enrichment
Automated enrichment transforms reactive security operations into proactive, intelligence-driven teams with measurable improvements.
Reduced alert fatigue and noise
Enrichment provides context to automatically validate or dismiss alerts, significantly reducing false positives. By applying confidence scoring and alert deduplication, you can cut through the noise and focus on genuine threats.
This directly addresses alert fatigue by ensuring your team only investigates alerts that have been validated through contextual analysis and vulnerability prioritization. Enrichment typically reduces false positive rates by 60-80%, allowing analysts to focus on genuine threats.
Accelerated investigation and response
Pre-enriched alerts eliminate manual research time during incidents. Investigators get complete context immediately, including threat origin, potential impact, and relationships to other activities in your environment.
Analysts can pivot from a runtime alert to related cloud identities, permissions, and exposed data stores in one view to speed containment. For example, when a suspicious process executes in a container, enrichment immediately reveals the pod's service account permissions, network exposure, and any sensitive data the workload can access—turning a generic "suspicious process" alert into a complete attack narrative with clear blast radius and remediation steps.
Organizations implementing this level of runtime-to-cloud correlation reduce investigation times from hours to minutes, enabling faster containment and reducing attacker dwell time.
Improved threat hunting capabilities
Enriched data enables proactive cloud threat hunting by revealing patterns and anomalies that indicate compromise. Hunters can pivot through enriched datasets to uncover hidden threats and reduce your attack surface before damage occurs.
Enhanced collaboration across teams
Enrichment provides common context that bridges gaps between security, development, and operations teams. When everyone shares the same understanding of a risk, remediation becomes faster and more effective.
Implementation challenges and solutions for threat intelligence enrichment
While enrichment provides clear benefits, implementation comes with specific challenges that you need to address.
Data quality and source reliability
Conflicting or outdated enrichment data from multiple sources can lead to incorrect conclusions and wasted effort. You need strategies for validating and prioritizing enrichment sources based on accuracy and relevance.
Source confidence ratings: Implement systems to validate intelligence sources based on historical accuracy
Data aging: Regularly review and retire underperforming feeds to maintain data quality
Intelligence source evaluation: Continuously assess the relevance and reliability of your threat feeds
Scale and performance considerations
Enriching high-volume cloud telemetry in real-time is computationally challenging and can create performance bottlenecks. You need architectural approaches that maintain performance at scale.
Cloud detection and response (CDR) capabilities integrated into a Cloud-Native Application Protection Platform (CNAPP) can handle this scale through distributed processing and progressive enrichment strategies, correlating runtime signals with cloud configuration context.
Integration complexity and tooling architecture
Integrating enrichment across diverse security tools requires understanding how different platforms interact:
Threat Intelligence Platform (TIP) role:
TIPs like Anomali, ThreatConnect, or MISP aggregate and normalize threat feeds, serving as the central enrichment source. They ingest STIX/TAXII feeds, commercial intelligence, and internal IOCs, then distribute enriched indicators to downstream tools via APIs.
SIEM integration patterns:
SIEMs like Splunk, Elastic, or Chronicle consume pre-enriched events from TIPs or perform enrichment lookups during query time. Bidirectional integration allows SIEMs to send novel IOCs back to TIPs for validation and distribution.
SOAR orchestration:
SOAR platforms like Palo Alto Cortex XSOAR or Swimlane trigger enrichment workflows during incident response—querying TIPs, sandbox services, and threat feeds to build complete attack timelines. Playbooks automate progressive enrichment based on alert severity.
CNAPP and CDR integration:
Cloud-native platforms integrate enrichment directly into detection pipelines, correlating cloud configuration data with runtime signals and external threat intelligence. This eliminates the need for separate TIP infrastructure in cloud-first environments.
Standardized formats and APIs:
STIX/TAXII: Use Structured Threat Information Expression (STIX) and Trusted Automated Exchange of Intelligence Information (TAXII) for standardized threat data exchange
OpenIOC: Leverage Open Indicators of Compromise format for cross-platform IOC sharing
RESTful APIs: Implement REST APIs for real-time enrichment lookups and bidirectional data flow
Modern architectures often combine TIP-based enrichment for external intelligence with CNAPP-native enrichment for cloud-specific context, creating a hybrid model that covers both traditional and cloud-native threats.
A unified resource model across AWS, Azure, GCP, and Kubernetes reduces enrichment toil and enables consistent policy and detections. Instead of maintaining separate enrichment logic for each cloud provider's native formats, normalized models let you write detection rules once and apply them across your entire multi-cloud environment.
Cost and resource management
Commercial threat intelligence feeds and enrichment platforms require significant investment. You need to balance enrichment coverage with operational costs based on risk and business priorities.
Best practices for effective threat intelligence enrichment programs
To maximize the value of threat intelligence enrichment, follow a structured approach that aligns with your specific security needs.
Establish clear enrichment requirements
Define what context is most valuable for your specific use cases before implementing any tools. Map enrichment sources to specific security outcomes like incident response or vulnerability prioritization.
Different teams need different types of enrichment—incident responders need deep forensic data while vulnerability management teams focus on exploitability and asset criticality.
Implement progressive enrichment strategies
Not every alert needs the same level of enrichment. Layer enrichment based on threat severity and investigation depth, applying lightweight enrichment for low-priority events and comprehensive enrichment for high-severity threats.
Use progressive enrichment—lightweight tags at ingest, deeper graph correlation for high-severity signals—to balance speed and fidelity at scale. For example, apply basic asset classification and reputation scoring to all events in real-time, then trigger deeper relationship mapping and attack path analysis only for alerts that exceed severity thresholds. This approach maintains sub-second enrichment latency for routine events while providing forensic-level context for critical threats.
Maintain enrichment data hygiene
Establish processes for validating, updating, and retiring enrichment sources. Create feedback loops where analysts can report inaccurate data to improve overall enrichment quality over time.
Companies like Synthesia maintain compliance standards through consistent enrichment and contextualization of security alerts, ensuring data quality across their security operations.
Governance, privacy, and compliance considerations
Enrichment pipelines handle sensitive security data and must align with regulatory and compliance requirements:
Data handling and privacy controls:
Implement access controls, encryption, and data minimization principles aligned with ISO 27001 and SOC 2 requirements. Enrichment systems should enforce role-based access control (RBAC) to limit who can view enriched threat data, particularly when it includes customer information or business-critical asset details. Define retention policies that balance forensic needs with data minimization—typically 90 days for routine alerts, 1-2 years for incidents.
Compliance framework alignment:
Map enrichment detections and response workflows to relevant frameworks:
NIST SP 800-61: Align enrichment outputs with incident response phases (detection, analysis, containment, recovery)
MITRE ATT&CK: Tag enriched alerts with ATT&CK tactics and techniques for consistent threat classification
CIS Controls: Demonstrate how enrichment supports continuous monitoring (Control 8) and incident response (Control 17)
PCI DSS / HIPAA: Show how enrichment helps identify and protect sensitive data exposure
Audit and accountability:
Maintain audit trails of enrichment decisions, including which sources contributed to risk scores and why alerts were escalated or dismissed. This supports compliance audits and improves enrichment accuracy over time through feedback loops.
Measure enrichment effectiveness
Track specific metrics that demonstrate enrichment impact on your security operations:
Operational efficiency metrics:
Mean time to detect (MTTD): Measure reduction in time from event occurrence to detection (target: 40-60% improvement)
Mean time to investigate (MTTI): Track time from alert to initial assessment (target: 50-70% reduction)
Mean time to respond (MTTR): Monitor time from detection to containment (target: 60-80% improvement)
Mean triage time: Measure analyst time spent per alert before escalation or closure
Detection quality metrics:
False positive rate: Track percentage of alerts closed as false positives (target: reduce by 60-80%)
Enrichment coverage: Measure percentage of alerts with complete context (≥5 enrichment fields populated)
Alert escalation rate: Monitor percentage of alerts requiring tier-2 or tier-3 investigation
Case closure rate: Track percentage of incidents resolved without escalation
Business impact metrics:
Analyst productivity: Measure alerts investigated per analyst per day
Escalations avoided: Count alerts auto-closed through enrichment validation
Cost per investigation: Calculate total enrichment cost divided by investigations completed
Risk reduction velocity: Track time from vulnerability discovery to remediation
Collaboration and remediation metrics:
Remediation ownership mapping: Track percentage of issues automatically assigned to responsible services, teams, or source code repositories
Code-driven remediation rate: Measure percentage of issues resolved through infrastructure-as-code or application code changes rather than manual console fixes
Developer engagement: Monitor time from security finding to developer acknowledgment and fix deployment
Track remediation ownership mapping to services/teams and the percentage of issues resolved via code changes to drive code-to-cloud improvements. Organizations that enrich findings with code repository and developer owner context see 50-70% faster remediation times and higher fix rates compared to generic vulnerability reports.
Organizations implementing comprehensive enrichment typically see 60-80% reduction in false positives, 50-70% improvement in MTTR, and 40-60% increase in analyst productivity within six months.
How Wiz transforms raw threat data into actionable cloud intelligence
Wiz redefines threat intelligence enrichment by embedding it directly into cloud security operations. Instead of treating enrichment as a separate step, Wiz integrates it into every stage of risk identification and response.
The Wiz Security Graph automatically enriches every security finding by correlating vulnerabilities, misconfigurations, and network exposure across your entire cloud environment. Wiz Threat Center enriches external threat intelligence with your specific cloud inventory to instantly show exposure to zero-days and emerging threats.
Toxic combinations surface enriched, prioritized risks by identifying critical attack paths that emerge from seemingly low-priority findings. Wiz Defend enriches runtime signals with cloud context through automated investigation timelines that transform raw alerts into coherent attack stories.
AI-powered investigation capabilities automatically enrich issues with attack narratives, impact analysis, and remediation guidance, and can generate queries or policies to accelerate response. For example, AI can translate a natural language question like 'show me all internet-exposed databases with admin credentials' into a graph query, or automatically generate a policy to prevent similar misconfigurations in the future. Cloud-to-code correlation enriches threat findings by tracing them back to source repositories and developer owners for faster remediation.
Ready to turn noisy alerts into prioritized, context-rich intelligence? Get a demo to see how a graph-powered, agentless approach makes detections instantly actionable—often reducing investigation time from hours to minutes.
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