The threat surface is no longeryour perimeter.

Situation. A sovereign client in the critical-infrastructure space was being targeted by an APT group with a multi-year pattern of activity. The CTI team had limited visibility into the adversary's staging, tooling, and intent. Existing commercial feeds were insufficient.

Challenge. The team needed to detect the adversary's staging activity before exploitation — when the IOCs were visible on the surface and deep sources, before they reached the client's perimeter.

Approach. The Cyber Threat Intelligence capability was configured for the client's threat profile. The dark-web monitoring layer was tuned to the adversary's known TTPs and infrastructure patterns. The deep-web collection was tuned to the leak sites, code repositories, and forums the adversary was known to use. The IOC correlation engine identified a cluster of staging infrastructure that matched the adversary's pattern. A Threat Hunt Hypothesis was generated from the TTPs. A detection rule (Sigma + Snort) was deployed to the SOC. The attribution confidence was Sherman Kent "likely" (0.74) — supported by three independent correlation signals.

Outcome. The detection rule fired on the client's perimeter within 14 days of deployment. The staging infrastructure was identified before exploitation. The client's IR team was able to block the IOCs, hunt for related activity, and brief the board on the averted intrusion. The post-event validation confirmed the attribution.

Lessons. CTI that operates only on commercial feeds is operating at adversary tempo. CTI that operates on surface + deep + dark, with attribution-grade correlation, operates at sovereign tempo.

Situation. A large enterprise client had a vulnerability management program that tracked CVSS score as the primary prioritization signal. The result: the team was patching 20,000+ vulnerabilities a year, with no measurable reduction in exposure.

Challenge. The team needed a way to prioritize vulnerabilities by exploitation probability, not just severity. CVSS is a severity score; EPSS is an exploitation probability score; KEV is the catalog of known-exploited vulnerabilities. The combination is more predictive than any one signal.

Approach. The Cyber Threat Intelligence capability was configured to ingest CVE / NVD / CWE / CVSS / EPSS / KEV daily. The vulnerability tracking module correlated each new CVE with the client's asset inventory, the EPSS exploitation prediction, the KEV catalog status, and the threat-actor landscape (i.e., which actors were likely to exploit this CVE). The output was a risk-based prioritization: a ranked list of vulnerabilities to patch, with a clear rationale for each.

Outcome. The first 90 days of the new prioritization framework saw a 64% reduction in the number of vulnerabilities the team was actively chasing, and a 38% increase in the number of vulnerabilities that had been actively exploited in the wild being remediated within SLA. The CISO's board narrative shifted from "we have 20,000 vulnerabilities" to "we are remediating the 200 that matter."

Lessons. Vulnerability management is a prioritization problem, not a counting problem. EPSS, KEV, and threat-actor correlation are the signals that turn counting into prioritization.

Situation. A SOC team had the right visibility (EDR, NDR, SIEM) but was being outpaced by an adversary using living-off-the-land techniques. The TTPs were known; the detection rules were not.

Challenge. The team needed detection rules generated from the adversary's TTPs, not from the adversary's specific IOCs. IOC-based detection is fragile; TTP-based detection is durable.

Approach. The Cyber Threat Intelligence capability mapped the adversary's known TTPs to the MITRE ATT&CK framework. For each technique, the capability generated detection hypotheses: what telemetry would be required, what behavior would be observed, what rule logic would fire. The detection rules were generated in Sigma, YARA, Snort, and Suricata formats, validated against a test environment, and deployed to the SOC. The rules were published in STIX/TAXII format for partner exchange.

Outcome. The first 60 days of TTP-based detection saw a 41% increase in true-positive detections, with no corresponding increase in false positives. The rules were shared with three alliance partners through STIX/TAXII exchange, who reported similar results. The adversary's campaign effectiveness was measurably degraded.

Lessons. IOC-based detection is brittle. TTP-based detection is durable. The MITRE ATT&CK framework is the lingua franca that makes TTP-based detection operational at scale.