The cybersecurity industry has an AI marketing problem. Every platform promises a revolutionary AI paradigm that magically solves security operations. If you sit in the SOC, however, you need answers instead of magic. Real questions need to be answered: Exactly how was the alert triaged? What data was looked at? What was the verdict before the attacker moved laterally?
Let's look directly at how autonomous AI changes the game where it matters most using these examples pulled directly from our public Casebook. Each was investigated entirely by our autonomous analyst, Agent Zero:
The Identity Problem: Three Attacks, Three Different Behaviors
Account compromise is rarely a single, uniform event. It disguises itself in the daily noise of your enterprise applications as shown in the following three examples:
1. The Covert Mailbox Pivot
In case run-e6d11c71, an attacker used a stolen session token to access an executive’s Exchange mailbox from an anonymous proxy. The threat actor executed 81 operations within Exchange Online in a compressed window and silently set up an inbox rule to intercept sensitive external emails.
2. The Multi-Stage Malware and DLP Event
In case run-71fdeb32, a technologist clicked a phishing link on day one. On day two, the compromised account triggered 30 distinct Data Loss Prevention violations involving sensitive technical specifications. The event capped off with an execution attempt of the Bearfoos malware.
3. The Impossible Travel Identity Threat
In case run-614f1635, an identity solution flagged an account logging in successfully 16 times across 7 different countries. The attacker used commercial VPNs to mask their location while consistently targeting a single application named Alpha. In a traditional SOC, these three alerts go to different queues. An analyst must pivot between Okta, Microsoft Exchange, DLP dashboards, and EDR systems while the hours tick by.
How Guided AI Logic Informs the Investigation
When we talk about AI in the SOC, we are talking about structured, deterministic investigation paths executed at machine speed. The autonomous analyst approaches the problem with a senior engineer’s mindset.
Scaling Data Queries Without Sampling
In the phishing and malware case, checking just the top 50 log lines misses the full scope of the compromise. Agent Zero queried 3,900 raw records across data sources in less than five minutes. An AI experiences no alert fatigue and looks at every single record to map out the entire blast radius.
Formulating Targeted Questions
An investigation depends entirely on the quality of the questions asked. For the mailbox compromise case, the AI asked 42 distinct, structured questions across the infrastructure. It verified the IP reputation, checked HaveIBeenPwned breach data, and validated session tokens to determine exactly how the attacker bypassed standard checks.
Calculating Physical Impossibilities
When investigating the identity threat across 7 countries, the AI calculated travel velocity between authentication timestamps. By instantly analyzing the Okta logs, it mathematically demonstrated a velocity violation. Logins from Phoenix and New York occurred just 49 minutes apart, which requires a speed of Mach 2.1 to achieve. The AI confirmed a VPN-masked compromise in 2 minutes and 3 seconds.
Real World Actions and Results
The metrics displayed for these examples as with all the Investigations on our site represent the actual output of an autonomous analyst on shift.
- Investigation Time: 2 minutes and 3 seconds for the Okta compromise.
- Questions Asked: 12 targeted investigative questions.
- Records Analyzed: 24 critical authentication records evaluated across Okta, Entra, and Defender.
When an incident occurs, a 5-minute autonomous verdict provides a massive advantage over a 5-hour manual investigation. It allows teams to contain the threat before a major data breach occurs.
The Human Element
We believe in eliminating employee overtime while keeping humans in control. By allowing an autonomous analyst to handle the tedious data aggregation (often requiring platform differentiated query construction), cross-referencing, and initial write-ups, human analysts can focus on high-level containment decisions and hunting for structural weaknesses.
This approach delivers rigorous, documented investigative reasoning. The work finishes before a human analyst can open the first ticket.
Explore the step-by-step logic, data sources, and questions used in these investigations by reading the full, unedited case breakdowns in the Command Zero Casebook.
