Augmenting your SOC with AI

Threat actors already use automation to probe and exploit environments. Defensive AI levels the playing field by triaging alerts, enriching incidents, and suggesting responses faster than humans can click through dashboards.

Sentinel + Copilot for Security

Microsoft Copilot for Security (GA in 2024) plugs into Sentinel, Defender, and Purview. The best results come when you:

• Craft custom prompt books that include your playbooks, naming conventions, and escalation matrix.
• Feed Copilot with pre-parsed logs from KQL queries so it has structured evidence.
• Set up guardrails: Copilot can draft remediation steps, but humans still approve containment or isolation actions.

Drop a demo video placeholder showing an analyst session.

Automating enrichment

Use Kusto to precompute the context Copilot and analysts need:

SecurityIncident
| where Severity == "High"
| extend Entities = parse_json(Entities)
| mv-expand Entities
| summarize Hosts = make_set(tostring(Entities.HostName)), IPs = make_set(tostring(Entities.Address)) by IncidentNumber

Feed that into Logic Apps or Azure Functions to automate enrichment with CMDB data.

Intel fusion

• Integrate Microsoft Defender Threat Intelligence feeds with your own honeypot observables.
• Train lightweight ML models to cluster incidents and spot campaign re-use.
• Use Graph Security API to synchronize context back into ticketing tools (ServiceNow, Jira).

Metrics to track

Monitor mean-time-to-detect, containment time, and false positive ratios. If AI is working, containment should drop below 30 minutes for your priority incidents.

Conclude with an invitation for a defensive AI tabletop exercise.