Attempt report

DLP flagged customer records pasted into an unsanctioned public AI chatbot

CybersecurityShadow AI Sensitive-Data ExposureDifficulty · Easy

Attempt 1 of 1 · cms4uel3700020jzroe1a8ijy

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Progress vs previous attempt

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Progression · Keep practicing

Stay on Easy · Cybersecurity

4 signals are blocking advancement to Medium. Keep practicing at Easy until those areas stabilize. (Track: Cybersecurity)

Track · Cybersecurity

Easy

Sample · 5 recent attempts2 positive4 blocking

Signals helping

Dangerous action frequency. None in recent attempts
Recent retry improvement trend. Score is improving (+5.2 pts on later attempts)

Signals blocking advancement

Recent average score. 34 / 100 (need ≥ 75)
Recent pass rate. 1 of 5 passed (need ≥ 66%)
Rubric category coverage. 36% average (need ≥ 55%)
Consistently weak rubric areas. Investigation, Evidence preservation, Attack understanding

Submission · what was sent and how you responded

Shadow AI Sensitive-Data ExposureDifficulty · easyHigh asset

DLP alert — customer data + code pasted into a public AI chatbot (shadow AI)

From: Maria Lopez <maria.lopez@acme-corp.com>
To: soc@acme-corp.com
Date: 2026-05-04 14:18 UTC

Hi SOC, Our DLP / web proxy flagged a possible data exposure and I want a second opinion before I talk to the user. A Support team member (jordan.kim) used a PERSONAL account on a public AI chatbot from their work laptop and pasted in what looks like a chunk of a customer export plus a snippet of an internal script. This is NOT one of our approved tools — it is "shadow AI". The paste was about 30 minutes ago. Jordan says they were "just trying to summarize a ticket backlog faster". I have the DLP match details and the proxy log. I have not contacted Jordan yet and I have not changed anything. What is the right order of steps, and how do I work out exactly what was exposed and who needs to know? — Maria (SOC, Tier 1)

Evidence

DLP match summary + web-proxy log excerpt (JORDAN-WKS)

# DLP match (endpoint agent)
time:      2026-05-04 13:47 UTC
user:      jordan.kim@acme-corp.com   host: JORDAN-WKS
action:    clipboard paste into browser -> chat.example-ai[.]com (category: Generative AI, UNSANCTIONED)
rule hit:  "Customer PII (name+email+phone)" x42 rows, "Source code (internal)" x1 block
sample:    [REDACTED by DLP] 42 rows matching <name,email,phone,acct_id>; ~60 lines of an internal helper script

# Web proxy (src=10.12.51.30 JORDAN-WKS)
13:46:55  CONNECT chat.example-ai[.]com:443   ALLOW (no GenAI category block configured)
13:47:10  POST    chat.example-ai[.]com/api/conversation   (request body not inspected — TLS)
13:51:02  GET     chat.example-ai[.]com/   200

# Tool / account context
- chat.example-ai[.]com = public consumer AI chat, PERSONAL login (not SSO, not in the app catalog)
- no enterprise data-retention setting, no DPA / contract with this vendor
- Support role scope: read access to the customer ticket system + a customer-export report

Affected asset

Name: Customer records (42) + internal script snippet via jordan.kim
Type: Regulated customer PII export + internal source snippet pasted into an unsanctioned public AI chatbot
Owner: Customer Support · Jordan Kim (data owner: Support Ops / Privacy)
Level: High

Your submitted response

451 words

1. Triage and scope. Treat this as a confirmed data exposure, not a suspected one: the DLP match already shows 42 rows of customer PII (name, email, phone, acct_id) plus ~60 lines of an internal helper script were pasted into an unsanctioned public AI chatbot at 13:47 UTC from JORDAN-WKS. Priority is high because the data is regulated customer PII and the vendor has no DPA and no enterprise retention controls.

2. Preserve evidence first, before anything is changed. Export and hash the DLP match record, the full web proxy log for src 10.12.51.30 around 13:40-14:20 UTC, and the endpoint agent logs from JORDAN-WKS. Preserve browser history and session artifacts on the laptop. Do not wipe, reimage, or let Jordan delete the chat conversation until legal and privacy have decided whether the conversation itself is evidence. Record a chain of custody with timestamps and who collected what.

3. Contain. Block chat.example-ai[.]com and the whole Generative AI proxy category for unsanctioned tools, since the proxy log shows no GenAI category block was configured. Do not immediately confiscate the laptop unless there is evidence of malicious intent - this looks like well-meaning misuse. Ask Jordan, in a non-punitive interview, to stop using the tool and to not delete anything. Review whether Jordan's customer-export report access is broader than the Support role needs and reduce it if so.

4. Investigate what exactly was exposed. Work from the DLP block sample to identify the specific 42 customer records and the exact script that was pasted. Determine which customers, which data fields, and which jurisdictions are involved, because that drives the notification obligation. Check whether the internal script contains any credentials, API keys, or internal hostnames - if it does, rotate those immediately and treat it as a second, separate incident. Check the proxy logs for other users and other GenAI destinations to find out whether this is one person or a pattern.

5. Recover and remediate. Request deletion of the conversation from the vendor and record their response, but do not assume deletion is effective - assume the data is out. Rotate any secret that appeared in the pasted script. Configure the proxy to block unsanctioned GenAI categories going forward and provide a sanctioned alternative so people are not pushed into shadow AI. Add a DLP rule that alerts on paste-to-GenAI in real time rather than after the fact.

6. Notify. Engage Privacy and Legal early with the concrete record count and field list so they can assess regulatory notification timelines. Inform the data owner in Support Ops. Keep the communication factual.

7. Follow up. Run a short awareness briefing for Support on approved AI tools, and add shadow AI to the periodic access review.

Final score

77/ 100

451 words submitted

Verdict · Pass

Solid response — your plan covers the core incident response steps and avoids dangerous actions. Score: 77/100. Strongest area: Clarity & structure (100%). Weakest area: Evidence preservation (33%) — expand this next time.

Category breakdown

Where points came from

coverage × weight = points

Attack understanding3/3 · 15.0 / 15
Asset impact3/3 · 10.0 / 10
Prioritization1/2 · 5.0 / 10
4/5 · 16.0 / 20
Investigation3/4 · 11.3 / 15
Recovery2/3 · 6.7 / 10
Evidence preservation1/3 · 3.3 / 10
Clarity & structure2/2 · 10.0 / 10

Strengths

Attack understanding
Asset impact
Investigation
Clarity & structure

Missing / weak

Evidence preservation

Dangerous actions detected

None detected in your response.

Learning · Coaching

Learn from this attempt

Post-submission coaching for this scenario. Score and verdict are unchanged — these notes are for your next attempt.

Why points were deducted

Evidence preservation33% coverage
Preserve (export, never delete) the DLP match and proxy logs, screenshot the tool/account, and record the record count for Privacy.
Prioritization50% coverage
Scope-before-notify: confirm exactly what was exposed first, then bring in the data owner and Privacy/Legal; keep it non-punitive.
Recovery67% coverage
Provide a sanctioned AI alternative, tighten DLP/proxy GenAI policy, and refresh acceptable-use training so the need is met safely.

Model answer outline

From: Incident Response Lead (SOC on-call)To: IT Leadership · Affected Asset Owner · On-call SOCSubject: [SEV-3][INC-CYB-E5061] DLP flagged customer records pasted into an unsanctioned public AI chatbot — status update

Incident · INC-CYB-E5061 · SEV-3 / P3Status · Investigating — containment in progressCybersecurity · Shadow AI Sensitive-Data Exposure · EasyCustomer records (42) + internal script snippet via jordan.kim · High criticalityDetected ~ 2026-05-04 14:18 UTC

Situation & summary

A Support user (jordan.kim) pasted ~42 rows of customer PII (name/email/phone/acct_id) plus a ~60-line internal script into a public consumer AI chatbot (chat.example-ai[.]com) from their work laptop, using a personal login on an unsanctioned tool with no DPA and no enterprise data-retention. DLP and the web proxy caught it ~30 minutes ago. This is a shadow-AI data-exposure incident, not malware — the job is to scope what left, contain further exposure, preserve the evidence, and bring in the data owner / Privacy.

Severity & priority

Rated SEV-3 / P3. Treat as a P2 confirmed data-exposure: regulated customer PII left to a third party with no contract, but it is bounded and already detected.

Prioritization & impact

Treat as a P2 confirmed data-exposure: regulated customer PII left to a third party with no contract, but it is bounded and already detected.
Scope what was exposed BEFORE deciding on notification — the record count and fields drive whether this is a reportable privacy event.
Loop in the data owner (Support Ops) and Privacy/Legal early; keep it factual and non-punitive so the user keeps cooperating.

Containment (actions taken / in progress)

Add a GenAI / unsanctioned-AI category block (and block chat.example-ai[.]com) at the proxy so the same paste cannot be repeated fleet-wide.
Tell Jordan to stop using the tool and not to paste the data again; do not have anyone re-enter the data to 'test' it.
Request deletion of the conversation from the vendor and opt out of any training use, and flag the 42 affected account ids to the data owner to watch.

Investigation (in progress)

From the DLP match and proxy log, establish exactly what was pasted (42 PII rows + which fields, and the internal script), not just that 'something' was.
Check whether Jordan (or others) did this before or with other GenAI sites — one paste or a pattern changes the response.
Identify the source of the export (which report / ticket query) so the data owner can confirm the records and classification.

Recovery & next steps

Stand up or point users to a sanctioned AI option so the productivity need that drove the shadow use has a safe path.
Tighten DLP / proxy policy for GenAI categories and add the lesson to acceptable-use / AI-usage policy.
Run a short, blameless awareness refresher for Support on what may and may not be pasted into external tools.

Evidence preservation

Preserve the DLP match record and the web-proxy log entries (export, do not delete) with the case id.
Capture a screenshot / record of the tool, account type, and timestamps before any policy change.
Record the affected record count and fields for the Privacy/Legal assessment.

Stakeholder communication

Brief the data owner (Support Ops) and Privacy/Legal with the concrete scope (42 records, fields, source).
Coach Jordan factually on what happened and what to do instead — reporting and cooperation should not feel punished.
Hold any external/customer notification until Privacy/Legal complete the reportability assessment.

Do NOT

Do not delete the DLP alert or clear the proxy logs — they are the evidence of what was exposed.
Do not re-paste the data into the chatbot to 'reproduce' it — that repeats the exposure.
Do not forward the exposed customer records around over email/chat while investigating.
Do not jump to discipline before scoping; punitive first moves discourage future reporting.

Dangerous actions to avoid

Do not delete the DLP alert or clear the proxy logs — they are the evidence of what was exposed.
Do not re-paste the data into the chatbot to 'reproduce' it — that repeats the exposure.
Do not forward the exposed customer records around over email/chat while investigating.
Do not jump to discipline before scoping; punitive first moves discourage future reporting.

How to improve next time

Shadow AI is a data-governance incident: the core question is always 'what data left, to whom, under what contract' — answer that before anything else.
Blocking the GenAI category at the proxy contains the whole fleet, not just one user; pair it with a sanctioned alternative so people do not route around it again.
Never reproduce a data-exposure by re-entering the data — you would be exposing it a second time.
Scope drives notification: the record count, fields, and customer identities determine whether Privacy/Legal must report it.
Keep shadow-AI response blameless and factual; punishing the first reporter teaches everyone else to hide the next one.

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Why did I get this score?

Your verdict was Pass at 77/100. That total is the sum of deterministic rubric points across 8 categories — each scores how much of its expected, ordered steps your answer covered, not an opinion about your writing. Your strongest coverage was Attack understanding (100%). Points were held back mostly in Evidence preservation (33%), Prioritization (50%), Recovery (67%).

Rubric focusevidencePreservationprioritizationrecovery

Next study step

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