AIAgentsThatReason,Execute,andLearn

Analyzing the request to triage all critical findings and check for active exploits. First, I need to scope the query — this requires accessing the unified findings database across all connected sources. Let me check which integrations are active. I can see Nessus, Qualys, and Burp Suite are connected with recent sync data. I'll query all three simultaneously to get a complete picture of the exposure surface. Starting with the findings query — filtering for severity: critical, state: open. This will pull from the normalized data model where findings from all tools have been deduplicated and enriched with asset context. Now cross-referencing against threat intelligence sources. I need to check NVD for CVE details, EPSS for exploit prediction scores, and CISA KEV for known exploited vulnerabilities. High EPSS scores combined with KEV entries indicate immediate risk. Found CVE-2024-1234 with EPSS score 0.94 — this has a 94% probability of exploitation in the next 30 days. Checking if there's a public exploit available... confirmed, active exploit in the wild. This needs immediate escalation. CVE-2024-5678 is on the CISA KEV list — this means it's been actively exploited in real-world attacks. Cross-referencing with our asset inventory to determine blast radius and business impact. Planning agent tasks: I'll need to spawn sub-agents for browser reconnaissance to validate web-facing exposures, and a sandbox shell agent to confirm exploitability of the network-level findings. Both can run in parallel. Determining ownership for auto-ticketing — analyzing code repository mapping to identify the developers responsible for the affected services. I'll create Jira tickets with full context, reproduction steps, and remediation guidance. Setting SLA timers based on severity and asset criticality. Critical findings on production assets get 24-hour SLA with automatic escalation to the security lead if unresolved.