Shadow AI incident response begins with logs that may already be gone

1. Executive summary

LevelBlue reports a consistent and growing operational gap in EMEA financial services: employees are using unsanctioned generative AI platforms ("shadow AI") to process corporate data, and when responders arrive to scope the resulting incidents, the network evidence of data egress has already rolled over and been lost. Firewall logs showing outbound connections to api.openai.com, claude.ai, and gemini.google.com — often the clearest proof of what data left the organisation — are frequently unavailable by the time incident response begins. The bottom-line risk for financial institutions is dual: irrecoverable loss of forensic evidence preventing accurate data-breach scoping, and regulatory exposure under GDPR and DORA for failing to implement defensible technical controls restricting employee AI usage.

2. Regulatory framing

No specific NIS2 or UK NIS article is directly engaged by this item beyond the general incident-management obligations already covered by the DORA articles above. The GDPR "appropriate technical and organisational measures" standard referenced by the source is noted but falls outside the provided regulatory reference scope.

3. Technical analysis & attack chain

This item is a strategic/operational advisory, not a vulnerability or threat-campaign report. The following describes how shadow AI incidents unfold based on the source material.

How the incident works

1. An employee accesses an external, unsanctioned AI platform — specifically api.openai.com, claude.ai, or gemini.google.com — from a corporate endpoint, pasting or uploading corporate data for processing.
2. The data leaves the organisation via standard HTTPS outbound connections through the corporate firewall. In many environments, these connections are not specifically logged, or the logs have short retention periods.
3. The incident is discovered hours or days later. By the time responders are engaged, firewall logs showing the outbound connections have already rolled over and are permanently gone.
4. If the data remained solely on the endpoint (e.g., in a local AI client or browser session), the evidence window is even shorter: every subsequent user action on the device risks overwriting memory-resident artefacts.
5. Responders request the user's endpoint to be secured and all relevant logs to be preserved. In the majority of cases, the organisation cannot locate the logs, discovers it was not logging the relevant events at all, or finds that retention has already expired.

Key technical observations from the source

• Evidence gap is systemic. The source states that the gap between what an organisation believes its logging environment captures and what it actually captures is "one of the most consistent things we see." Organisations have, on multiple occasions, returned three days after a log request to report they cannot find the logs or never stored them.
• Firewall egress logs are the first to disappear. Outbound connection records to AI platforms are the highest-value evidence and the most time-sensitive. These are the clearest indicator of what data left the organisation and when.
• Endpoint evidence is fragile. If evidence exists only on the user's endpoint — particularly in memory — the speed of endpoint isolation is the determining factor in whether evidence survives.
• No specific CVE, malware, or threat actor is involved. This is an insider/usage risk, not an external intrusion. No attribution to a named threat actor applies.

Confidence caveat: All technical observations are single-sourced, drawn from a Help Net Security interview with Brandy Wityak, VP of Complex Matters at LevelBlue. No independent corroboration is available in the provided material. The claims are consistent with established incident-response practice but should be verified against the client's own environment before enforcement.

4. Mitigation & containment

P1 — Within 24 hours

• Identify all outbound egress to known AI platforms. Query existing firewall, proxy, and DNS logs for connections to api.openai.com, claude.ai, and gemini.google.com before evidence rolls over. If logs are still available, export and preserve them immediately.
• Secure endpoints of any employees identified as having accessed AI platforms in the scope of an active investigation. Isolate from the network if data-loss assessment is ongoing. Do not allow the user to continue operating the device if memory-resident evidence is relevant.
• Verify logging is actually enabled. Do not assume. Confirm with the network team that firewall egress logs, proxy logs, and DNS query logs are actively recording and that retention periods are sufficient (minimum 90 days for financial services incident-response needs).

P2 — Within 72 hours

• Implement egress filtering or alerting for AI-platform domains. At minimum, create SIEM alerts for outbound connections to api.openai.com, claude.ai, gemini.google.com, and other sanctioned/unsanctioned AI endpoints. If the business has not approved these platforms, block at the proxy or firewall.
• Extend log retention. Set firewall, proxy, and DNS log retention to a minimum of 90 days, aligned with incident-response and regulatory reporting timelines. If current retention is 7–30 days (common default), this is the gap that loses evidence.
• Inventory all AI usage. Conduct a retrospective review of available logs to identify which employees or teams are using external AI platforms and what data categories may have been exposed. This scoping is necessary for any potential GDPR or DORA reporting obligation.

P3 — Within 7 days

• Convert AI usage policy into enforceable technical controls. The source explicitly flags the gap between a policy in a wiki and a control "a company can defend." Implement technical restrictions — proxy blocks, DLP rules for AI-platform domains, endpoint agent policies — that demonstrably prevent or log unsanctioned AI usage.
• Document the control framework. Regulators assess whether the organisation took measures within its power to restrict employee activity and mitigate risk. Maintain evidence that controls were implemented, tested, and operating — not merely that a policy existed.
• Tabletop exercise. Run a shadow AI incident scenario testing log preservation speed, endpoint isolation workflow, and the ability to produce evidence a regulator would expect.

5. Indicators of compromise

No atomic indicators of compromise are available in the source material. The source describes observable behaviours and legitimate domains used in shadow AI incidents, not malicious artefacts.

Behavioural indicators

6. Detection

Insufficient indicators to author detection rules.

The source material identifies legitimate AI-platform domains as behavioural indicators but does not provide malicious artefacts (strings, file hashes, command-line arguments, registry keys, or process names) that would constitute a threat-specific YARA or Sigma rule. Detection should be implemented as network egress alerting and DLP rules for the domains listed in §5, not as threat-artefact signatures.

7. Sources

• Help Net Security, "Shadow AI incident response begins with logs that may already be gone," https://www.helpnetsecurity.com/2026/07/28/brandy-wityak-levelblue-shadow-ai-incident-response/, 2026-07-28

8. Adverse Trace position

This is a medium-severity operational risk for EMEA financial services clients, escalating to high severity in the event of an actual data-loss incident where evidence has been lost. The core problem is not a novel threat actor or exploit — it is a logging and control deficiency that prevents organisations from scoping incidents and defending themselves to regulators. The source is single-sourced (LevelBlue via Help Net Security) but the observations are consistent with established IR practice and align with patterns we see across the sector. Clients should treat this as an immediate log-retention and egress-monitoring gap: if your firewall and proxy logs for outbound AI-platform traffic are not retained for at least 90 days, you will likely be unable to scope a shadow AI incident or demonstrate regulatory compliance when one occurs. We will monitor for additional reporting corroborating or expanding on these findings and will update clients if specific incident cases or additional AI-platform indicators emerge.

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Published via PulseTrace — Adverse Trace threat intelligence.