AI Security · Enterprise Data Protection

Protecting Critical Data in Copilot, Enterprise Chat and Shadow AI

Three channels your data leaves by, what to detect and prevent on each, and a four-week runbook that ends with a test anyone can understand.

Microsoft 365 Copilot · enterprise Claude / ChatGPT · shadow AI~8 min read
In this piece
  1. Three channels your data leaves by
  2. A four-week runbook
  3. References

Three channels your data leaves by

Most organisations are not building agents yet. They are rolling out Copilot, standing up an enterprise Claude or ChatGPT tenant, and quietly discovering how much shadow AI is already in use.

Those are three different problems with three different answers. Ranked by what you actually hold: with Copilot you have a control, with sanctioned enterprise chat you have a contract, and with shadow AI you have nothing at all.

Copilot

Inside the tenant. Nothing leaves — but everything the user can read is now findable.

Copilot inherits the user’s permissions. A decade of ‘anyone with the link’ sharing becomes a search index.
DETECT
  • Oversharing report on SharePoint and OneDrive — files on org-wide or anonymous links
  • Purview audit of Copilot interactions
  • DLP alerts on prompt and response content
PREVENT
  • Remediate permissions before rollout, not after
  • Restricted SharePoint Search as a fence while you clean up
  • Sensitivity labels on HR, finance, legal and M&A sites
Sanctioned enterprise chat

Claude or ChatGPT on a commercial tier. Data leaves, under contract.

Terms govern training, retention, region and subprocessors — but only if the tenant setting matches the contract.
DETECT
  • Read the agreement: training use, retention, subprocessors, region
  • Screenshot the live tenant setting beside the contract clause
  • Connector and integration inventory; egress DLP on the API path
PREVENT
  • Commercial or enterprise tier only — never a consumer tier for business data
  • Configure region and retention before first use
  • Connector allow-list; disable file upload where not needed
Shadow AI

Personal accounts on managed devices. Data leaves, with no contract at all.

Consumer terms commonly permit training on inputs. There is no DPA, no retention control and no audit trail.
DETECT
  • Proxy, DNS or CASB: traffic to consumer AI domains from managed devices
  • Entra app-consent and OAuth grants to unsanctioned AI apps
  • Expense reports — individual subscriptions charged to a card
PREVENT
  • Give people a sanctioned tool first; blocking without an alternative fails
  • Block consumer AI domains at the proxy once the alternative exists
  • Browser policy against pasting labelled content into unmanaged sites
The takeaway Copilot does not leak data — it finds the data you already over-shared. Permission remediation is the control, and it is work you owed anyway. For the other two channels the test is just as blunt: if the contract clause and the live tenant setting disagree, you do not have a control — you have a promise. And shadow AI has neither.

A four-week runbook

What to actually do, in order, with a test at the end that gives an unambiguous answer.

Week 1
Measure before you enable
  • Run the oversharing report across SharePoint and OneDrive
  • Pull 90 days of proxy or CASB data for consumer AI domains
  • List every AI tool already in use, including ones bundled into existing licences
OUTCOMEA number you can show leadership. Expect thousands of over-shared files.
Week 2
Remediate the crown jewels
  • Remove org-wide and anonymous links on HR, finance, legal and M&A sites
  • Apply sensitivity labels to those sites, not to individual files
  • Name a data owner per site — remediation without an owner does not stick
OUTCOMEThe files that would end a career are no longer reachable by search.
Week 3
Fence the rest and give people a path
  • Restricted SharePoint Search scoped to remediated sites only
  • Provision the sanctioned enterprise tier and tell everyone it exists
  • Verify the vendor tenant setting matches the contract clause; screenshot both
OUTCOMEA sanctioned route exists, so blocking the unsanctioned one becomes reasonable.
Week 4
Try to break it
  • As a pilot user with ordinary permissions, ask for ‘salary bands’ and ‘termination letters’
  • If either returns, stop the rollout and go back to week 2
  • Only then block consumer AI domains at the proxy
OUTCOMEAn unambiguous pass or fail that a non-technical executive can understand.

Then keep it closed — five standing controls

Monthly delta

Oversharing report re-run monthly. Track the trend, not the absolute number.

Alerts to the owner

Route DLP alerts to the named data owner, not only to IT. IT cannot judge whether a file is sensitive.

Annual re-verification

Re-check every vendor tenant setting against the contract, and after any vendor migration.

One written rule

What may never leave: CUI, PHI, privileged material, source code, unreleased financials.

Records coverage

Retention, legal hold and eDiscovery extended to prompts and responses, not just files.

Order matters Do not block first. Blocking consumer AI before a sanctioned tool exists moves the traffic to phones and home laptops, where you cannot see it at all. Sanction, then fence, then block — in that order.

References

Primary guidance

Careful Adoption of Agentic AI Services — ASD’s ACSC, CISA, NSA, the Canadian Centre for Cyber Security, NCSC-NZ and NCSC-UK, 30 April 2026. The first coordinated Five Eyes guidance on agentic AI; source of the five risk categories and the system diagram. cisa.gov · full PDF

Industry research

Cloud Security Alliance, AI Safety Initiative — research notes on the joint guidance and the 2026 agentic identity survey: 82% of organisations have found unknown AI agents in their environment, 65% had an agent security incident in the past twelve months, and 21% have a formal agent decommissioning process. labs.cloudsecurityalliance.org

Frameworks used for mapping

OWASP Top 10 for LLM Applications — source of the OWASP identifiers on every risk row. The guidance also cites OWASP Top 10 for Agentic Applications (2026), worth reviewing alongside it.

MITRE ATLAS™ — Adversarial Threat Landscape for AI Systems; source of the AML.T identifiers, and named in the guidance as a recommended threat-modelling taxonomy.

NIST AI Risk Management Framework — source of the GOVERN / MAP / MEASURE / MANAGE subcategory references. Those mappings are our derivation, not an official crosswalk.

Also cited in the guidance

NIST SP 800-207 Zero Trust Architecture · CISA 2025 Minimum Elements for a Software Bill of Materials · CISA AI Cybersecurity Collaboration Playbook · NCSC-UK Guidelines for Secure AI System Development · MIT STAMP / STPA-Sec materials

Verification note. OWASP and MITRE ATLAS identifiers change between published versions — check both against the current release before using them in an assessment. The NIST AI RMF subcategory mappings are our derivation; NIST has not published a crosswalk at this granularity. Products named throughout are examples of a control category, not recommendations or an endorsement.

Diagram redrawn from Figure 1 of the joint guidance. © Commonwealth of Australia 2026, licensed CC BY 4.0.

On the product names. Feature names reflect Microsoft 365 and major AI vendor offerings at time of writing. Tiers, names and availability change; verify in your own tenant before relying on any of it.