This worked case belongs to the false-positive investigation closure guide. Its job is specific: one case, every decision, and the record each decision left.
The case in one paragraph (Illustrative)
Illustrative. A 310-person managed security provider monitors a client's file servers. The client changes its backup product, and the new backup agent runs at 01:00 under a dedicated service account, reading about 180,000 files across six servers in forty minutes. A detection for mass file reads followed by archive creation, a common ransomware staging pattern, fires on every server. The activity is legitimate and approved. The hard part is not deciding that; it is deciding how to stop the noise without blinding the rule to a real attacker who might later use the same pattern.
Step by step: who decided what, and which record shows it
| Step | Decision | Who decided | Record it left | System |
|---|---|---|---|---|
| 1. Alert | Rule version 21 fires six times in ten minutes | Detection logic | Alerts with rule name, version, host, account, alert IDs | Detection platform |
| 2. Triage | Group the six alerts into one case and raise priority | Tier 1 analyst | Case with linked alert IDs, priority change, timestamp | Case manager |
| 3. Evidence | Match service account, signed backup binary and the client's change ticket for the new product | Tier 2 analyst | Case notes, attached query results, change ticket reference | Case manager |
| 4. Verdict | Close as harmless: real activity, expected, not malicious | Tier 2 analyst, approved by shift lead | Classification and reason, closing comment, analyst and approver IDs | Case manager or SIEM |
| 5. Proposal | Exclude the six servers from the rule at night | Detection engineer | Pull request with the proposed change | Rule repository |
| 6. Review | Reject: a host-wide exclusion would hide a real staging attack on those servers | Detection engineering lead | Review comment and rejection | Rule repository |
| 7. Fix | Narrow exclusion to the signed binary hash plus the service account, with a 90-day expiry | Detection engineer, approved by lead | Rule version 22, rationale, approval, expiry date | Rule repository |
| 8. Check | 30 days later: no repeat alerts from backups; a test archive job from another account still fires | Shift lead | Review note and test result | Case manager |
Steps 3, 6 and 8 carry the judgment. Step 3 is the reasoning that separated expected from malicious. Step 6 is the decision not to take the easy fix. Step 8 is evidence that the chosen fix worked and did not over-suppress. Many teams record step 4 and nothing else.
Why this sequence is useful evidence
A false positive has two possible failures: the noise continues, or the fix silences a real attack. The case above records how a team steered between them, and each record answers a question an outside reviewer would ask.
- Was the verdict reasoned? The case notes in step 3 show the specific facts checked: account, binary signature, change ticket.
- Was the label meaningful? Platforms use different vocabularies. Microsoft Sentinel closes incidents with a classification such as Benign Positive and a reason such as Suspicious But Expected, or a False Positive with Incorrect Alert Logic. This case is the first kind, not the second: the rule did what it was written to do.
- Was the fix proportionate? The rejected proposal and the narrow approved change show a risk judgment. Where rules live in a repository, the review tool keeps that judgment: on GitHub, a reviewer can comment, approve, or request changes the author must address before merging.
- Did it hold? The 30-day check with a negative test is the only record that proves the exclusion did not create a blind spot.
- Was it time-limited? Sentinel automation rules, for example, can carry an expiration date. An expiry on the exclusion forces a later re-check, which leaves yet another record.
GitHubNIST's revised incident response guidance, SP 800-61 Revision 3 (April 2025), frames incident response as part of cybersecurity risk management rather than a separate activity. A closure history with proposals, reviews and checks is what that looks like in day-to-day records. For why AI developers value exactly this kind of exception reasoning, see exception handling data for AI agents.
Variations a partner will meet
The same shape repeats with different triggers. Recognizing it is more useful than memorizing one case.
| Trigger that looks malicious | What made it legitimate | The hard decision |
|---|---|---|
| Backup agent reading thousands of files | Approved product change, signed binary | How narrowly to exclude |
| Vulnerability scanner attempting logins across hosts | Scheduled authenticated scan from a known range | Whether to exclude the range or the scanner account |
| Admin tool running remote commands at night | Patch window under a change ticket | Time-boxed suppression or permanent tuning |
| Bulk mailbox export | Legal hold request from counsel | Who can confirm the request was genuine |
| Logins from two countries an hour apart | Traveling consultant on a corporate VPN exit | Whether travel data may be consulted at all |
How to recognize similar cases at a client
Ask the SOC director or detection lead these questions. None requires seeing a case.
- Do closed cases link back to the alert IDs that opened them?
- Is the closing comment free text with reasoning, or only a label?
- Are rule changes kept in a repository with review comments, including rejected proposals?
- Do exclusions carry expiry dates and recorded re-checks?
- How many years do those records span, and in which systems?
If most answers are yes, the company may hold complete exception histories. The system map of closure records shows how the joins work, and the referral brief template turns the answers into a one-page introduction. The cybersecurity services fit page explains which security records are usually in scope; complaint handling records show the same problem, action and result pattern in a different function.
The introduced company needs 50+ full-time employees at peak (contractors excluded), a documented operating history, rights to license the records, and an authorized contact. Companies in the U.S., Canada and the EU qualify, as do companies in the UK, Australia, New Zealand, India, Singapore, South Africa, Latin America, the UAE and Saudi Arabia when at least 90% of the data is in English. What stays unknown until SourceX reviews it: which records the company has rights to license, how client-derived details would be removed, whether there is dataset demand, and any offer.
Next step
Refer a company. Earn up to $100,000. Default reward: 25% of Actual Supplier Payout, capped at $100,000 cumulatively per referred company. Eligibility and payment conditions apply, and a signed Schedule A or addendum can differ. A lead, meeting or signature alone does not trigger payment.
If a security client keeps its tuning proposals, reviews and post-closure checks alongside its cases, register as a partner, check the company against who qualifies, and read the program terms before you introduce its leadership.