INVESTIGATION RECORD LAYER FOR SOC TEAMS
See how Casebook connects evidence, preserves what remains unexplained, and turns an analyst's investigation into reusable company knowledge. Compiled, not trained — same input → same output, always.
or deeper walkthroughs, synthetic scenarios, and pilot workflows
Compiled, not trained — no learned weights at runtime. Deterministic.
Memory that compounds — Case Files stay alive as your team evolves.
Every claim shows its work — SHA-anchored provenance, named human author.
Your senior analyst investigates a tricky alert at 2am. They figure it out. They fix it. They write a post-mortem in Confluence. Six months later, that person leaves. The post-mortem is stale. The reasoning is gone. The next analyst treats the same alert like the first one.
Splunk doesn't solve this — Splunk tells you what matched, not what it means or why it matters. Confluence doesn't solve this — Confluence documents die the minute they're written. Every SIEM in the market solves the detection problem. Nobody solves the memory problem.
Four steps. No new SIEM to learn. Casebook sits beside Splunk, not on top of it.
Drop a Splunk log. Casebook shows what happened. Reality only — no interpretation yet.
Casebook activates the patterns it knows and shows every finding with what it observed, what it expected, and how far it deviated.
What your team knows becomes durable. Vocabulary, expected behaviors, baselines. Teach once, applies forever.
Every Case File is signed, sealed, and stays alive as your Casebook Agent evolves.
The runtime handles the underlying structure under the hood. You just teach it your team's expertise.
No learned weights at runtime — none. Casebook is a compiler: it turns human-authored knowledge into a deterministic runtime. Same input → same output, always. Explain your reasoning to the CISO in a language auditors accept.
Every investigation your team completes becomes structural memory the next investigation can use. Similar-case retrieval finds prior patterns. Baselines learn from your environment. Institutional knowledge stops dying when people leave.
Click any finding. See the exact events that triggered it. See the detection rule set that recognized the pattern. See the named human who taught that pattern. Cryptographically signed. Auditor-ready.
40 failed logins on alice from 203.0.113.14 over 6 minutes — brute-force pattern
What if this is wrong?
Every finding in Casebook looks like this. Not a black-box confidence score. A specific claim, with its evidence, its baseline comparison, its human author, and its own falsifying conditions.
This is what "compiled, not trained" makes possible. A neural network can't tell you why it flagged an alert. Casebook can — and will show you exactly what would change its mind.
Casebook is a compiler + runtime for operational knowledge. Not a model. Not a copilot. The closest architectural analog is LLVM, not GPT.
A construction-time neural network proposes typed structure — new evidence types, new patterns, new mappings. A human approves. A deterministic walker verifies. The safety checks gate the change. Then the runtime executes on your evidence with zero neural networks present — same input, same output, forever.
The receipts
If you've ever tried to defend a machine-learning alert to a CISO, an auditor, or a customer's legal team, you know the exact shape of the problem Casebook was built to solve.