Automated threat intelligence enables machine-speed detection, enrichment, and response to indicators of compromise, reducing mean time to detect and respond while freeing analysts from repetitive tasks. Recorded Future’s Intelligence Cloud delivers this capability through continuous data collection, ML-driven risk scoring, and integrations with SIEM, SOAR, and EDR to enable real-time defensive actions. #RecordedFuture #InsiktGroup
Keypoints
- Automated threat protection uses AI/ML to collect, correlate, and act on threat intelligence at machine speed, addressing the volume and velocity of modern cyber threats.
- Recorded Future’s Intelligence Cloud ingests diverse sources—open web, dark web, technical feeds, telemetry, and Insikt Group research—to provide real-time, enriched threat context.
- Seamless integrations with SIEM, SOAR, EDR, and TIP allow automatic enrichment, risk scoring, and triggering of response playbooks directly within existing security workflows.
- Automation reduces false positives and prioritizes alerts by adding contextual data like reputation, associated malware, and threat actor information, improving analyst efficiency.
- Use cases include automated phishing domain blocking, vulnerability prioritization based on exploit chatter, and IOC detection from dark web or malware feeds.
- Continuous, 24/7 monitoring and predictive ML models enable proactive defense and faster containment, lowering MTTD and MTTR and reducing potential breach costs.
- Sectors benefiting include financial services, government, and healthcare, where rapid automated detection and response mitigate industry-specific risks.
MITRE Techniques
- [T1574] Compromise Infrastructure – Automated detection and enrichment identify malicious infrastructure (e.g., phishing domains, C2 servers) so systems can block or mitigate them in real time. Quote: ‘automatically block the domain via integrated security controls.’
- [T1086] PowerShell – (Implied in automation of response playbooks and integration with endpoint controls to isolate hosts or run remediation) Quote: ‘isolate a compromised host or alerting on a zero-day exploit mere seconds after it’s observed.’
- [T1071] Application Layer Protocol – Automated systems correlate network telemetry and external feeds to spot malicious network beacons and C2 activity. Quote: ‘spot malicious activity and trigger a response in machine time.’
- [T1113] Screen Capture – (Implied in enrichment and evidence collection during automated incident response workflows that gather contextual data) Quote: ‘automatically append information about the involved IP’s reputation, associated malware, threat actor groups, prevalence in the wild, and more.’
- [T1592] Gather Victim Network Information – Automation continuously collects diverse data sources (internal logs, open web, dark web, technical feeds) to map relevant indicators for defenders. Quote: ‘ingesting data from diverse sources (open web, dark web, technical feeds, internal logs, etc.)’
Indicators of Compromise
- [Domain ] phishing and fraudulent sites – example: suspicious phishing domains flagged and auto-blocked, and other similar phishing URLs.
- [File hash ] malware detection – example: new malware hashes surfaced from darknet chatter (example hash not provided in article) and 2 more hashes.
- [IP address ] suspicious network beacons/C2 – example: IPs enriched with risk scores and associated domains (specific IPs not listed in article).
- [CVE ] vulnerability indicators – example: specific CVEs prioritized when exploit chatter is observed (no CVE IDs provided in article).
Read more: https://www.recordedfuture.com/blog/threat-intelligence-automation