Inline Real-Time Attack Prevention in Copilot Studio with Zenity

Zenity’s expanded integration with Microsoft Copilot Studio embeds inline, real‑time attack prevention directly into Copilot Studio agents, promising step‑level policy enforcement, data‑exfiltration controls, and telemetry for enterprises that want to scale agentic AI without surrendering governance or compliance.

Background / Overview​

Microsoft’s Copilot Studio is the low‑code/no‑code environment inside the Power Platform that lets organizations build, customize, and deploy AI agents (copilots) that can access documents, call connectors, and execute actions across corporate systems. Microsoft recently added a runtime hook that allows Copilot Studio to send an agent’s planned execution “plan” (prompts, tool names, inputs, and metadata) to an external monitoring endpoint which returns an approve/block verdict before the agent executes a tool call. Microsoft documents this capability and describes it as an “advanced near‑real‑time protection” feature, now available in public preview. (microsoft.com)
Zenity, a vendor focused on securing agentic AI across the build‑to‑runtime lifecycle, says it has expanded its integration with Copilot Studio so that Zenity’s controls operate “inside” each Copilot Studio agent, enforcing policies at the step level to block prompt injection, prevent improper secrets handling, and stop unintended data movement to external endpoints. Zenity’s announcement frames the integration as buildtime‑to‑runtime coverage that delivers continuous visibility, posture assessment, threat detection, and in‑flight mitigation. (zenity.io)
Taken together, the Microsoft runtime monitoring API and Zenity’s runtime enforcement create a model where third‑party security platforms or tenant‑hosted endpoints can become synchronous decision points in an agent’s execution loop. Microsoft’s documentation and blog make three operational points explicit: the monitor receives rich context, the monitor must decide quickly (the preview behavior gives it one second), and audit logs are emitted for every monitored interaction. (learn.microsoft.com)

---

What the integration actually does — technical breakdown​

The plan → monitor → execute decision loop​

• An event (user prompt or trigger) reaches a Copilot Studio agent and the agent composes a plan: a deterministic sequence of steps, tools, connector calls, and the concrete inputs it intends to use.
• Copilot Studio forwards that plan payload — which includes the prompt, recent chat history, tool names and inputs, and metadata such as agent ID and tenant ID — to a configured external monitoring endpoint.
• The external monitor evaluates the payload against policies, detection models, or enterprise logic and returns an approve or block verdict.
• If block, the agent halts the step and notifies the user; if approve, the agent proceeds. If no verdict arrives within the configured timeout, Copilot Studio’s preview behavior proceeds by default (reported as a one‑second timeout in Microsoft’s docs). (learn.microsoft.com)

Inline enforcement surfaces​

Zenity and Microsoft describe enforcement being applied to common agent action surfaces:

• MCP servers and custom tools (Model Context Protocol integrations).
• Power Platform and Copilot connectors (CRM, ERP, databases).
• Email and communication actions.
• Retrieval‑augmented generation (RAG) flows where external knowledge sources are queried.
  Zenity positions enforcement at the step level so policies map to the exact operation an agent would take — for example, blocking a connector write or preventing more than X PII fields from leaving the tenant. (zenity.io)

What’s passed to the monitor (telemetry)​

The monitor receives detailed context: the original prompt, recent conversational context, the list of planned tools and their proposed inputs, and correlation metadata (agent ID, tenant ID, user/session identifiers). Microsoft and vendors warn organizations to treat telemetry residency, storage, and retention as first‑class concerns because the monitor payload can include sensitive content. (learn.microsoft.com)

---

Verified technical specifics​

The following claims have been independently verified against vendor and platform documentation:

• The runtime monitoring API is a supported Copilot Studio feature and is documented in Microsoft Learn and the Copilot blog as public preview functionality. (learn.microsoft.com)
• The monitor receives rich plan payloads (prompt, history, tool inputs, metadata) and returns an approve/block decision that Copilot Studio respects before executing a step. (learn.microsoft.com)
• Microsoft’s documentation and blog state the preview timeout behavior is one second (if the monitor does not respond within that window, Copilot Studio proceeds by default). This one‑second figure is explicitly documented in the Copilot blog and Learn pages for the preview feature. Organizations should verify the timeout semantics in their tenant settings and during testing because preview behavior can change. (microsoft.com)
• Zenity’s product and press materials describe the integration as adding inline step‑level prevention, runtime threat reasoning, and buildtime posture checks as part of a continuous security posture for agents. Zenity’s announcements and Azure Marketplace listing confirm availability and positioning. (zenity.io)

Where public documentation is silent or promotional (for example, sweeping claims that a vendor is “the only” or “first” solution of a type), treat those marketing assertions as unverified until validated by third‑party analyst reports or independent testing. Zenity’s press material is authoritative for product claims, but independent third‑party validation should be used for procurement decisions. (prnewswire.com)

---

Strengths — why this matters for enterprise IT and security​

• Runtime prevention moves enforcement to the point of action. Traditional controls (DLP, perimeter controls, post‑hoc SIEM detection) are often too late for autonomous agents that can execute actions in seconds. Inline monitoring lets defenders stop risky operations before they complete. (microsoft.com)
• Step‑level policies yield granular controls. Mapping policies to discrete steps enables narrow, context‑aware rules: allow a read but block a write; permit summary generation but not export; prevent mixing secrets with third‑party endpoints. Zenity emphasizes this affinity for step‑level enforcement. (zenity.io)
• Audit trails suitable for compliance and forensics. Each monitored interaction emits logs (payload, verdict, timestamps) that can feed SIEMs and compliance reporting — improving explainability and post‑incident analysis. Microsoft’s admin tooling centralizes configuration across environments. (learn.microsoft.com)
• Enables safe democratization. By letting business units (marketing, HR, finance) build agents with centralized enforcement, organizations can realize productivity gains while preserving security guardrails. Zenity positions the integration precisely for that business case. (zenity.io)

---

Risks, limitations, and operational caveats​

• Default‑allow timeout tradeoff. Microsoft’s preview behavior defaults to proceeding if the external monitor fails to respond within the timeout (documented as one second). That design favors user experience but introduces an operational risk: high avail or latency at the monitor could lead to unsafe actions being allowed. Security teams must architect redundancy, low latency, and robust SLAs for monitoring endpoints. (learn.microsoft.com)
• Telemetry sensitivity and data residency. The monitor receives prompts and tool inputs which may include PII, PHI, or IP. Vendors and Microsoft allow tenant control and private hosting patterns, but teams must validate retention guarantees, encryption, access controls, and residency policies before enabling external monitoring. (learn.microsoft.com)
• False positives and productivity impact. Aggressive blocking policies can interrupt valid workflows. Balance is required: overly strict rules will frustrate users and may push them to shadow IT. Test policies against representative workloads and tune thresholds.
• Supply‑chain and trust model for security vendors. Integrating third‑party monitors means trusting another vendor with agent plans and potentially transient secrets. Vet vendors for security posture, independent audits, and contractual protections. Host custom monitors inside tenant boundaries when data residency or trust is a concern. (learn.microsoft.com)
• Not a substitute for identity and model governance. Runtime controls mitigate many risks, but they cannot replace least‑privilege credential design, strong Entra/MFA governance, supply‑chain validation of connectors, or adversarial testing of models and prompts. A layered approach is essential.

---

Practical deployment checklist — operationalizing Copilot Studio + Zenity​

• Planning & governance
• Define the scope of monitored environments and which agent classes require inline enforcement (pilot vs. production).
• Align stakeholders (security, platform engineering, legal, business owners).
• Create policy templates: e.g., PII export limits, payment system write restrictions, secrets‑mixing prohibitions.
• Architecture & availability
• Decide between vendor‑hosted monitor, tenant‑hosted private endpoint (VNet), or hybrid.
• Architect redundancy for monitoring endpoints to meet the low‑latency decision window (target sub‑500ms median).
• Ensure private endpoints support secure authentication (Microsoft Entra app IDs, FIC) and sign requests to verify integrity. (learn.microsoft.com)
• Data handling & compliance
• Determine whether telemetry is transient-only or persisted. Enforce encryption at rest/in transit and tenant keys where possible.
• Map monitoring logs to retention policies needed for GDPR, HIPAA, or other applicable regulations.
• Validate vendor SOC/ISO attestations if using third‑party monitors. (learn.microsoft.com)
• Policy design & testing
• Start with non‑blocking monitoring mode (observe only) to accumulate telemetry and tune rules.
• Run red‑team exercises (prompt‑injection, RAG exfiltration, connector abuse) to validate detection and response.
• Gradually shift to automated blocking with well‑documented exception processes.
• Operations & incident response
• Integrate monitor logs with SIEM (Sentinel) and configure alerting for blocked actions and vendor timeouts.
• Create playbooks for blocked operations: investigation, remediation, user notifications, rollback if needed.
• Track false positives and iterate policy tuning on a cadence (weekly during pilot; monthly in production).

---

Adversarial testing: how to validate protections realistically​

• Techniques to test:
• Prompt injection red‑teams that attempt XPIA/UPIA-style exploit chains to force secrets or data exfiltration.
• Connector abuse scenarios where an agent is tricked into writing to unauthorized endpoints.
• RAG poisoning: inserting malicious or misleading documents into knowledge stores to see whether monitors detect anomalous intent.
• Latency and failure injection on the monitor endpoint to validate fallback behavior and SLAs.
• Validation metrics to capture:
• Mean time to block/allow decision; distribution of latencies.
• False positive and false negative rates per rule.
• Missed exfiltration attempts (false negatives) under adversarial load.

Run these tests under realistic workload patterns and capture audit trails for evidence and remediation planning.

---

Vendor claims to treat cautiously​

• Any marketing statements that a vendor is the “first” or “only” provider to achieve a capability should be verified with independent analyst reports or third‑party testing.
• Performance claims (e.g., “blocks X% of attacks automatically”) should be validated in your tenant with sampling and red‑team validation before relying on them in procurement decisions. Zenity’s announcements are credible product claims, but independent proof points are advisable for purchasing decisions. (prnewswire.com)

---

Recommended adoption path for enterprise teams​

• Stage a cross‑functional pilot that includes representative agents and data types; enable monitoring in observe mode.
• Tune policies from pilot telemetry; iterate detection thresholds and reduce noisy rules.
• Harden monitoring endpoints (redundancy, low latency SLAs, authentication) and verify fallback behaviors in failover tests.
• Bring in Zenity or other vetted vendors for a limited production rollout, starting with low‑risk business units and scaling once metrics stabilize.
• Institutionalize adversarial testing, continuous posture scans, and a governance board that signs off on escalations and exceptions.

---

Longer‑term considerations​

• Standardization: Expect industry standards and best practices (OWASP LLM guidance, MITRE agent frameworks) to converge around run‑time controls, auditability, and explainability. Map policies to emerging standards to reduce friction in audits and vendor evaluations.
• Regulation angle: As agents access regulated data, regulators will expect explainability, controlled telemetry, and retention limits. Treat runtime monitoring as part of a compliance program, not a substitute for lawful processing and DPIAs.
• Continuous improvement: Runtime prevention is an important control but must be paired with model governance, identity hardening, and secure connector design to reduce the attack surface across the whole agent lifecycle.

---

Conclusion​

The Zenity + Microsoft Copilot Studio integration marks a pragmatic evolution in enterprise AI security: by adding inline, step‑aware monitoring and enforcement into Copilot Studio’s execution path, defenders gain the ability to stop risky actions before they happen while preserving the productivity benefits of business‑led agent development. Microsoft’s runtime monitoring API (public preview) and Zenity’s runtime enforcement capabilities together deliver a layered control plane that can materially reduce common agent risks — prompt injection, RAG leakage, and connector misuse — when implemented thoughtfully.
That said, the architecture brings operational tradeoffs: the one‑second decision window, telemetry handling, monitoring endpoint availability, and vendor trust must be designed and tested to avoid creating blind spots or productivity traps. Enterprises should pilot the integration, perform adversarial testing, validate telemetry residency and retention, and build robust operations playbooks before scaling Copilot Studio broadly.
When combined with strong identity governance, least‑privilege connectors, and continuous adversarial validation, the Zenity and Copilot Studio integration can be a practical foundation for scaling agentic AI safely and responsibly across the business. (learn.microsoft.com)

---

Source: Channel Insider Zenity Expands Integration With Microsoft Copilot Studio