AI features are no longer optional extras. They’re embedded in the everyday tools your teams use: “What’s on my calendar?”, “Summarize unread messages”, “Draft responses”, plugins that integrate LLMs into internal dashboards. That means trust is no longer something you hope your users extend. It’s something your systems implicitly ask for and sometimes misuse.
Recent incidents and research show this implicit trust has real, material risk. Consider the MCP connectors exploit: OpenAI’s ChatGPT Gmail & Google Calendar connectors were shown via compromised calendar invites to leak sensitive emails.
A malicious event description (metadata) was enough. A user asks ChatGPT to look at the calendar. Hidden instructions ride along. Data gets siphoned out. The user never clicks anything dangerous; the trust was already granted.
Then there's EchoLeak, a vulnerability in Microsoft 365 Copilot. It’s a zero-click, prompt-injection exploit allowing remote data exfiltration no user action, because some content was auto-fetched or interacted with via proxy and content-security policies. Researchers found it bypassed several defenses, showing that even widely deployed copilots are vulnerable at trust boundaries.
These examples aren’t edge-cases. Prompt injection, data leakage, insecure integrations are showing up across many environments.
Researchers are showing how attackers are using types of prompt injection to leak data from internal APIs, traverse trust boundaries, and exploit agent-behaviors.
What This Means for How We Govern Trust
Because trust is being embedded by default, often invisibly governance can’t wait for the “ideal state.” As a CISO, these are things you need to think about deeply:
- Trust surfaces are expanding
Calendars, email, auto-summaries, document collaborations all potentially leak points. The vector isn’t just “use of LLMs” but “how connected tools integrate data.”
- Implicit permissions + invisible metadata = exploited channels
These aren’t user-click phishing attacks. These are flows where the user authorized the connector once and then expects safety. The metadata (event notes, invite descriptions) becomes a conduit for instructions.
- Existing controls don’t see into model context
Even good defences like input filtering, access control, or URL isolation fail if they don’t inspect the model’s full context (what it’s allowed to read; the content of metadata; what plugins or agents are involved).
- Zero-click and indirect prompt injection are rising
It’s not always something the user does. Sometimes it’s what the AI environment does automatically (auto-fetching data, scanning calendar entries, responding to queries that traverse multiple services). EchoLeak is a solid recent example.
- Regulatory expectations are shifting to require oversight, audit, and traceability
Laws and proposals like the EU AI Act, NIST overlays, etc., increasingly expect that when AI systems touch sensitive data, there is clear documentation, audit logs, proof of governance (who saw what, when, why), and the ability to respond when something goes wrong.
Concrete Measures CISOs Should Be Starting Now
To make “trust” a managed, visible variable not a hidden assumption, these are more detailed, operational steps worth embedding immediately:
- Map all AI-enabled integrations
Not just sanctioned “chatbots” or agents, but all tools (connectors, plugins, copilots) that read data from your systems or write back to them. Include calendar invites, metadata sources, document summarization tools.
- Inventory data sensitivity & assign policy tiers
Define what data categories (e.g. internal docs, customer PII, IP, financials) need stricter handling. Establish policies accordingly: for high sensitivity, block auto-shared summaries, require human review before AI uses.
- Harden connectors and agents with trust-boundary controls
Ensure metadata cannot carry rogue instructions. Input filtering but also context analysis. For example: event descriptions or invite fields should be considered untrusted content by default. Reject or sanitize suspicious content inside those fields.
- Use prompt partitioning and least privilege models
Agents/capabilities should only get what they need: if a model doesn’t need access to email, don’t give it. If it needs calendar data, allow only specific fields. Enforce role-based permissions for AI-linked data access.
- Continuous adversarial testing (red teaming AI)
Regularly simulate attacks: prompt injections, malicious metadata, scenario based attacks like calendar invites with hidden instructions. Assess which guardrails hold.
- Guardrails baked in
Build real-time enforcement so that policy violations are prevented or flagged by system, not just looked for after the fact.
- Audit trails & monitoring with context
Logs should capture not only who used what, but what data was pulled, what content was read, what metadata was included in prompts. Monitoring should detect anomalous usage patterns: e.g. a copilot suddenly accessing content in many shared calendars.
How Quilr helps build trust in the age of AI
In the AI era, Quilr builds from those very needs. We aim to make trust visible, auditable, and controllable, not just aspirational.
- Quilr’s discovery mechanisms surface unsanctioned or semi-sanctioned AI integrations, letting you see where implicit trust has been granted.
- Our guardrails intervene where risk is high: metadata sanitization, prompt filtering, role-based context enforcement.
- We support policy-as-code so your trust rules (like “no PII in auto-summary,” or “metadata must be clean before use”) are enforced automatically.
- Quilr’s audit traces include fine-grained context: what data was considered, what access was used, which agent acted and show you where governance has gaps.
Trust is fast becoming one of the primary variables in enterprise risk, not a soft issue, but something attackers are explicitly targeting. Implicit trust through connectors, metadata, default permissions has turned many AI features into potential liabilities.
For CISOs, the imperative is to shift from reacting to visible security incidents toward proactively governing where trust is placed. From inspecting manifest app permissions to auditing agent behavior and metadata all of which need visibility and enforcement baked in.
Because with AI, being trusted implicitly can be the quietest path into a breach. Managing trust is the new edge in risk.
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