incident-response-trainer
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Attempt report

DLP flagged customer records pasted into an unsanctioned public AI chatbot

CybersecurityShadow AI Sensitive-Data ExposureDifficulty · Easy

Attempt 1 of 1 · cmqkcz4ug00020jyhh35zo9y1

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Progress vs previous attempt

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Progression · Keep practicing

Stay on Easy · Cybersecurity

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

Track · Cybersecurity
Easy
Sample · 5 recent attempts2 positive3 blocking

Signals helping

  • Dangerous action frequency. None in recent attempts
  • Rubric category coverage. 67% average (need ≥ 55%)

Signals blocking advancement

  • Recent average score. 65 / 100 (need ≥ 75)
  • Recent pass rate. 1 of 5 passed (need ≥ 66%)
  • Recent retry improvement trend. Score is regressing (-11.5 pts on later attempts)
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
1 word
Test
Final score
0/ 100
1 words submitted
Verdict · Fail

The response is missing several critical incident response steps. Review the rubric and try again. Score: 0/100. Strongest area: Clarity & structure (2%). Weakest area: Attack understanding (0%) — expand this next time. The response is quite short; aim for a more structured, step-by-step plan.

Category breakdown

Where points came from

coverage × weight = points
  • Attack understanding0/3 · 0.0 / 15
  • Asset impact0/3 · 0.0 / 10
  • Prioritization0/2 · 0.0 / 10
  • 0/5 · 0.0 / 20
  • Investigation0/4 · 0.0 / 15
  • Recovery0/3 · 0.0 / 10
  • Evidence preservation0/3 · 0.0 / 10
  • Clarity & structure0/2 · 0.2 / 10

Strengths

No category reached 70% coverage.

Missing / weak

  • Attack understanding
  • Asset impact
  • Prioritization
  • Investigation
  • Recovery
  • Evidence preservation
  • Clarity & structure

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

  • Containment0% coverage

    Block the GenAI category / the site at the proxy, stop the user, and request vendor deletion / training opt-out — not just 'talk to Jordan'.

  • Attack understanding0% coverage

    Name this as shadow-AI / unsanctioned-tool data exposure (sensitive data sent to a third-party GenAI service), not a malware or account-compromise incident.

  • Investigation0% coverage

    Use the DLP match + proxy log to pin the exact records and fields, check for repeat / other-tool use, and trace the source export.

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.
AI · Supplemental review

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Review language
AI Tutor · Explains your result

AI Tutor

This tutor explains your result. It does not change your score. Pick a question to see how the deterministic grading reached your verdict and where to focus next.

Generated deterministically from your graded result — no AI model was called.

Why did I get this score?

Your verdict was Fail at 0/100. 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 Clarity & structure (2%). Points were held back mostly in Containment (0%), Attack understanding (0%), Investigation (0%).

Rubric focuscontainmentattackUnderstandinginvestigation
Next study step

Re-read the containment expectations for this scenario and list the concrete steps you missed.

This tutor explains your existing result. It does not change your score, verdict, or grade. Generated deterministically from your graded result — no AI model was called.

What should I improve first?

Focus on Containment first — it is your weakest rubric area at 0% coverage and carries weight 20. For this scenario: Block the GenAI category / the site at the proxy, stop the user, and request vendor deletion / training opt-out — not just 'talk to Jordan'.

Rubric focuscontainment
Next study step

Rewrite your containment section as a short numbered checklist before your next attempt.

This tutor explains your existing result. It does not change your score, verdict, or grade. Generated deterministically from your graded result — no AI model was called.

How does my answer compare to the model answer outline?

Compared with the model answer outline, the most useful sections to study are the ones matching your weak areas. Re-read the outline's containment, attack understanding, investigation guidance and check which listed points you did not cover. The outline is a high-level checklist of expected points — use it to find gaps, not to copy a finished answer.

Rubric focuscontainmentattackUnderstandinginvestigation
Next study step

Pick one model-answer section you missed and add its key points to your next response in your own words.

This tutor explains your existing result. It does not change your score, verdict, or grade. Generated deterministically from your graded result — no AI model was called.

Which rubric area mattered most here?

Containment mattered most here: it carries the highest rubric weight (20), so coverage there moves your score the most. You covered 0% of it this time, worth 0 points.

Rubric focuscontainment
Next study step

Prioritise the highest-weight categories first; make sure containment is fully addressed before lower-weight ones.

This tutor explains your existing result. It does not change your score, verdict, or grade. Generated deterministically from your graded result — no AI model was called.

What should I study next?

Based on this attempt, study containment, attack understanding, investigation next. Coaching tip for this scenario: 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.

Rubric focuscontainmentattackUnderstandinginvestigation
Next study step

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.

This tutor explains your existing result. It does not change your score, verdict, or grade. Generated deterministically from your graded result — no AI model was called.

Coach Notes

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