Securing Copilot: Runtime Data Leakage Risks and Enterprise Defenses

Microsoft’s Copilot rollout has delivered a leap in workplace productivity—and with it, a fresh class of security risk that is only visible when the assistant is actually running. Recent disclosures and vendor analyses show a practical, repeatable pattern: configuration hardening, identity controls, and static DLP reduce risk, but they don’t fully close the gap that opens when an LLM-driven assistant synthesizes and returns information at runtime. That blind spot—what the assistant grounded its answer on and what it actually returned to the user—now sits at the center of enterprise AI security conversations. ://www.varonis.com/blog/reprompt)

Background: why Copilot changes the threat model​

Copilot (in its consumer and enterprise variants) is not a conventional application. It’s a retrieval-augmented generation (RAG) stack that combines user context, tenant content, and large-language capabilities to synthesize answers on demand. A single prompt can cascade into multiple backend retrievals, produce inferred facts from disparate documents, and recompose outputs in ways humans do not always anticipate.
Microsoft’s own documentation explains the grounding process and the kinds of data Copilot can draw from—work files, emails, chats, calendar events, and (when enabled) web sources—subject to user identity and access controls. That retrieval step is where the risk concentrates: it determines which artifacts actually informed a reply.
Traditional enterprise controls were designed for a different era:

• Deterministic access paths (explicit requests for specific files or services).
• Static permission models (role or group-based access that doesn’t change by prompt).
• Endpoint and network telemetry wired for connections and file accesses—not for synthesized AI outputs.

When an assistant composes responses using many small data extracts, the decision about what to include happens at runtime. That qualification makes “post-configuration” security checks necessary but not sufficient—runtime visibility and enforcement become essential.

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The case that crystallized the problem: Reprompt​

In mid‑January 2026, Varonis Threat Labs published a proof‑of‑concept that exposed a real-world manifestation of the runtime blind spot: an exploit they named Reprompt. The researchers described a three-stage chain—Parameter‑to‑Prompt (P2P) injection, a double‑request repetition bypass, and chain‑request orchestration—that allowed a malicious deep link to open a Copilot session and then quietly harvest tiny fragments of data over repeated follow‑ups. The entire pipeline required only a single click.
Independent reporting confirmed the mechanics and timeline: several security outlets reproduced the technical claims and documented Microsoft’s remediation in the January 2026 Patch Tuesday updates. The vulnerability primarily affected Copilot Personal (consumer) experiences in the initial disclosures, while enterprise Microsoft 365 Copilot tenants benefited from additional tenant-level controls such as Purview auditing and DLP.
Why Reprompt matters beyond the headline:

• It shows how trusted vendor-hosted flows (deep links, prefilled prompts) can be weaponized.
• It demonstrateent logs and edge telemetry can miss contextual prompts and subsequent server‑side follow-ups.
• It proves the attacker model is low friction (phishing link in an email or chat) and stealthy (exfiltration in micro‑chunks to avoid volume-based detection).

Those properties expose the precise limitation Aryaka and others highlight: a gap between what configuration and post‑hoc telemetry tell you and what actually happens when Copilot answers a live query.

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What Microsoft already offers — and where it helps​

Before concluding that enterprises are helpless, it’s important to map the controls already in the ecosystem and their scope.

• Microsoft Purview: Microsoft has developed DLP for Microsoft 365 Copilot that can block processing of prompts containing sensitive information and prevent Copilot from using files or emails with specific sensitivity labels. Purview also provides auditing, insider‑risk indicators, and one‑click policy templates for Copilot activity. These controls can prevent or curtail some classes of leakage at runtime, particularly for tenant-managed Copilot instances.
• Retrieval and telemetry APIs: Microsoft exposes retrieval APIs and audit telemetry that allow administrators to query which items were considered during grounding (subject to permissions and API availability). That provides a path toward reconstructing the evidence trail for a given Copilot interaction—if the tenant has configured and retained the right signals.
• Endpoint and identity controls: Conditional Access, MFA, session controls, and device management significantly reduce the attack surface by restricting who can invoke Copilot and from which devices or networks. These remain indispensable.

Taken together, these tools reduce exposure and create auditability—but they are not universal panaceas. Coverage varies by product variant (consumer vs enterprise), some features remain in preview in certain regions or license SKUs, and enforcement depends heavily on correct configuration and labeling discipline.

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Where the blind spot persists​

Even with the Purview and retrieval toolset, practical gaps remain that make runtime enforcement and visibility a pressing need:

• Consumer surface vs. tenant surface: Many mitigations are aimed at Microsoft 365 Copilot (tenant managed). Copilot Personal / consumer experiences historically lacked the same DLP/policy hooks and were the initial target of Reprompt-style chains. Enterprise risk increases when employees mix personal and work accounts on the same device.
• Prompt-injection and deep-link mechanics: Deep links that prefill prompts (the “q” parameter) are a convenience feature that attackers can weaponize. Client-side telemetry often records only that a link was clicked; it may not capture the full prompt text, subsequent server-driven follow‑ups, or the exact set of artifacts Copilot used to ground the response without explicit runtime capture. (varonis.com)
• Partial DLP coverage and labeling burdens: Purview’s protections rely on sensitivity labels, content detection, and policy scoping. If labels are missing, misapplied, or not comprehensive across older documents, network shares, or third‑party connectors, Copilot can still surface sensitive material. DLP that blocks prompts is powerful but not a substitute for correct metadat
• Semantic exfiltration and micro‑chunking: Attacks that exfiltrate data in tiny, contextual fragments can evade volume‑based DLP thresholds and leave minimal forensic traces in conventional egress monitoring. Without runtime semantic inspection of LLM exchanges, these patterns are easy to miss.
• Telemetry blind spots and cross‑service correlation: Standard M365 telemetry captures access and activity events, but does not always map neatly to why a retrieval occurred or which exact sentences were copied into an answer. For many security teams, reconstructing a malicious chain requires stitching multiple logs and tenant traces together—if those traces exist and are retained.

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Evaluating runtime inspection products: what they promise​

Vendors have responded to this problem with products that claim to provide the missing layer: real-time, inline inspection and enforcement of AI assistant traffic. The headline capabilities these products list typically include:

• Inline observation of Copilot prompts and responses across web, desktop, and mobile clients.
• Real‑time blocking or redaction of risky prompts and responses before they reach the model or the end user.
• Visibility into grounding: enumerating which files, chats, or URLs contributed to a specific answer.
• Validators for prompt-injection, PII/PHI detection, IP/code leakage detection, and content tone/safety enforcement.
• Unified dashboards with transaction‑level evidence for audits and investigations.

Those controls would, in theory, convert assumption‑based governance into evidence‑based governance: instead of inferring risk from config, you can see what the assistant actually did at each interaction. The value proposition is unmistakable for security teams that must prove compliance and respond to regulators and auditors.

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But there’s no free lunch: technical and governance tradeoffs​

The reality of intercepting Copilot traffic at runtime raises a raft of technical, operational, and legal tradeoffs. Any organization considering an inline inspection layer needs to evaluate these carefully.

• TLS / encryption handling and the meaning of “inline”
• To inspect Copilot traffic in transit, a security product must decrypt and re‑encrypt TLS (traditional SSL inspection) or run as a local agent (browser/office plugin) that observes content before TLS. Both models have downsides.
• TLS interception raises privacy and legal concerns: decrypted content includes sensitive personal data and secrets. If the inspection point is compromised, the plaintext can be exposed. Jurisdictional laws (GDPR, sectoral privacy rules) may limit or condition the legality of such interception.
• Identity, tokens, and session integrity
• Copilot sessions rely on user identities, OAuth tokens, and tenant-level authorization. Any MITM that tampers with tokens or header values can break authentication, violate terms of service, or generate false positives/negatives. Proper handling of tokens and re‑signing of requests is non‑trivial.
• Feature completeness and false positives
• Real‑time redaction of an LLM’s response is challenging because models synthesize content in ways that are semantically fuzzy. Aggressive redaction can significantly degrade assistant usefulness and create user friction; lax rules reintroduce leakage risk.
• Scale and latency
• Inline semantic inspection is CPU and memory intensive. At enterprise scale, latency and cost become material factors. Poorly‑implemented solutions will introduce delays, timeouts, or disrupted workflows.
• Evidence and non‑repudiation
• If the inspector claims to “prove” grounding, it must provide tamper‑evident logs and chain‑of-evidence that satisfy auditors. This requires secure, high-integrity telemetry pipelines, consistent time stamping, and retentiuct and vendor risk
• Entrusting runtime inspection to a third party centralizes new sensitive data flows. The inspector itself becomes a high‑value target. Organizations must vet vendor security, breach history, and contractual protections.

All of the above are practical realities, not theoretical objections. Enterprises must weigh them against the visibility benefits of runtime enforcement.

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How to think about AI>Secure‑style claims (practical checklist)​

When a vendor positions an inline, MITM inspection layer as the missing piece for Copilot security, apply this checklist before deployment:

• Coverage mapping
• Which Copilot variants are supported (Copilot Personal, Microsoft 365 Copilot, Copilot in Outlook/Word/Teams)? Does the vendor rely on network inspection, local agents, or browser extensions? Confirm exact client/platform coverage and limitations.
• Authentication and token handling
• How does the product preserve OAuth tokens and tenant identity? Does it re‑sign requests? Who holds private keys/certificates? What is the failure mode if the inspector is unavailable?
• Data governance and legal mapping
• Will the inspector decrypt PII/PHI and store it in logs? Where are those logs hosted, who manages access, and are retention policies compliant with GDPR/CCPA/HIPAA (as applicable)? Obtain contractual guarantees around data handling, breach notification, and audit rights.
• Integration with tenant controls
• Can the inspector feed its findings into Microsoft Purview, SIEM, or M365 audit logs? Or does it operate in parallel, creating duplicate evidence streams that must be reconciled?
• Performance and user experience
• Measure end‑to‑end latency in a test pilot. Verify that blocking or redaction rules degrade user workflows acceptably and that overrides/audit trails exist for business exceptions.
• Security of the inspector
• Ask for independent third‑party security assessments, SOC 2 reports, and a breach history. The inline inspector becomes high‑value infrastructure; treat it accordingly.
• Regulatory and contractual compatibility
• Confirm that inspection does not violate service agreements with Microsoft or other vendors and that it is acceptable under industry/regulatory contracts.

This checklist helps move purchasing decisions from marketing promises to operational clarity.

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Practical, prioritized steps for Windows and enterprise admins​

No single control eliminates Copilot risk. The most resilient posture is layered and pragmatic—patch quickly, lock down what you can, instrument what you must, and test continuously.
Short-term (first 7–30 days)

• Install vendor patches and emergency fixes. Apply Microsoft’s January 2026 updates and any behind‑the‑scenes Copilot hardenings. Patch windows were used to remediate Reprompt-style issues; rapid patching reduces exposure.
• Audit which Copilot variants run on corporate devices; block Copilot Personal on managed machines and prefer tenant‑managed Microsoft 365 Copilot for work data.
• Configure Purview DLP for Copilot prompts and block processing of sensitive information types in prompts. Enable DLP rules that prevent Copilot from processing files with certain sensitivity labels.
• Shorten session lifetimes and tighten Conditional Access policies for accounts that can use Copilot. Reduce token scope where possible.

Medium-term (30–90 days)

• Expand sensitivity labeling coverage in SharePoint, OneDrive, and critical file shares; use encryption labels where practical.
• Deploy tenant‑level telemetry collection and integrate Copilot audit signals with your SIEM and incident playbooks.
• Run red‑team exercises focused on prompt injection, deep‑link vectors, and micro‑chaining exfiltration scenarios.

Considerations for runtime inspection (90+ days)

• Evaluate runtime inspection providers against the checklist above. Run scoped pilots that assess performance, legal fit, integration, and false positive rates.
• If deploying inline inspection, limit scope where possible (e.g., endpoints accessing Copilot via unmanaged networks) and combine with endpoint protection for layered defenses.
• Prepare incident response runbooks that include Copilot-specific artifacts: grounding extracts, conversatvalidator logs.

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Conclusion: behavior matters as much as intent​

The Copilot era forces a change in the way we secure enterprise knowledge: it is no longer enough to intend to protect data by policy and configuration. We must verify how AI behaves in practice when interacting with that data. The Reprompt chain and related incidents are a wakeup call—a concrete demonstration that runtime behavior can undo careful configuration.
Microsoft’s platform-level controls (Purview, retrieval APIs, and tenant auditing) are critical and should be the foundation of any strategy, but they are not a full substitute for runtime visibility—especially when consumer‑grade surfaces and mixed‑account devices are in play. Inline inspection products promise to fill that final gap, but they introduce their own technical, privacy, and governance tradeoffs that must be evaluated carefully against organizational risk tolerance.
For WindowsForum readers and security teams: prioritize patching, tenant hardening, and Purview-based policies now; treat runtime visibility as the next security frontier and validate any runtime inspection vendor against strict privacy and operational criteria before wide deployment. In a world where an assistant can synthesize and leak data in real time, seeing what the assistant actually did is the essential condition for secure, auditable AI adoption.

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Source: Security Boulevard Microsoft Copilot Security Has a Blind Spot — And It’s at Runtime