Editorial Standards & AI Transparency

We believe in being explicit about this. Threadlinqs uses AI to assist in producing threat reporting. An automated research pipeline monitors a broad set of sources and drafts the initial structured report for each threat — the summary, indicators of compromise, MITRE ATT&CK mapping, CVE/CWE enrichment, and a first pass at detection logic — so that the feed keeps pace with a fast-moving threat landscape and analysts are freed from repetitive first-draft work.

AI is a drafting and enrichment tool here, not the final authority. Every threat published on the platform is subject to the human review process described below. We label this clearly because readers deserve to know how the content they rely on is created, and because security decisions should never rest on unreviewed machine output.

Threadlinqs runs a hybrid pipeline that pairs automated speed with human judgment. The lifecycle of every published threat follows four stages:

To be precise about the timing, because it matters for how you should read freshly posted items: a report appears in the feed first, and the human-in-the-loop review happens after posting, not before. The team of detection engineers and threat intelligence analysts reviews posted threats and then revalidates each one roughly a month after it was published. The revalidation pass re-examines whether indicators are still live, whether detection logic still fires correctly, and whether the threat's exploitation status or vendor remediation has changed.

• Primary sources first. We prioritize vendor security advisories, official CVE records, CISA KEV listings, and first-party incident reporting over secondary commentary.
• Enrichment is cross-referenced. CVE severity uses CVSS, exploitation likelihood uses EPSS, and known-exploited status is cross-checked against CISA KEV rather than asserted.
• Attribution is labeled by confidence. Threat-actor attribution reflects the confidence of the underlying reporting; we do not present low-confidence association as established fact.
• Indicators are scoped. IOCs are categorized (network, file, behavioral) and tied to the threat they were observed in, so context travels with the indicator.

Detection content is held to engineering standards, not just published as text:

• Three flavors, correctly typed. Rules are provided in Splunk SPL, Microsoft KQL, and Sigma, with each rule classified by its actual query language rather than assumed.
• Mapped to ATT&CK. Detections are mapped to MITRE ATT&CK techniques so coverage and gaps are measurable.
• Reviewed for efficacy. Detection logic is part of the human review and the one-month revalidation — a rule that no longer reflects current adversary behavior is flagged for update.

Threat intelligence is perishable. When new information changes a report — a revised severity, a retracted indicator, a corrected attribution, or an updated detection — we update the underlying record rather than leaving stale content in place. Reports that have been materially updated are marked accordingly in the platform and in our daily debriefs.

If you believe something we have published is inaccurate, out of date, or incorrectly attributed, please tell us. Corrections and responsible-disclosure reports can be sent via our contact page, and we treat accuracy reports as a priority.