Technical Risk Assessment Template for Accepting Desktop AI Agents into Corporate Networks

Hook: Your users want desktop AI — but who gets access to your files and network?

Desktop AI agents (like Anthropic's Cowork) promise huge productivity gains: automatic note synthesis, spreadsheet generation, code suggestions and file organization. But they also bring a concentrated attack surface: local file-system access, persistent processes, cloud-model interactions, and telemetry that can leak secrets. Security teams and IT need a repeatable, technical way to evaluate these apps before they touch corporate endpoints.

Executive summary — top recommendations (read first)

Do not allow a desktop AI agent on production endpoints without a technical risk assessment, pilot controls, and contract-level security guarantees. At minimum, require: restrictive network policies, host-based containment (sandboxing/VM), scoped file access, strong authentication (SSO + device posture), telemetry & logging, and contractual SLAs for data handling and vulnerability disclosure.

Use the template below to produce a scored risk register you can present to leadership and procurement. This document is tuned for 2026 realities: desktop agents with local file access are common, NIST AI RMF, EU AI Act guidance and EU regulation enforcement are maturing, and vendor transparency (SBOMs, model provenance) is increasingly required.

Why this matters in 2026 — short context

By late 2025 and early 2026, multiple vendors released desktop AI agents that operate with elevated local capabilities. These apps streamline knowledge work but also shift sensitive data processing from cloud-only to hybrid local/cloud models. Regulatory scrutiny increased: frameworks (NIST AI RMF, EU AI Act and national guidance) and procurement guidance now expect demonstrable controls for data flows, model updates, and vendor governance.

"Desktop AI agents combine the convenience of local assistants with the risk vector of a user-mode service that can read and write files and call external APIs."

How to use this template

1. Run an inventory: who wants the app, which endpoints, and what data will be in scope.
2. Fill the risk register rows for the product under assessment. Score likelihood and impact (1–5).
3. Prioritize mitigations and define gating conditions for pilot and full deployment.
4. Use the procurement checklist as contract requirements and acceptance tests.
5. Run a time-boxed pilot with monitoring and an incident playbook; iterate before broad rollout.

Risk assessment template (technical)

Copy these fields into your risk register (CSV/Excel/GRC tool). Each entry should include a mitigating control and responsible owner.

Risk scoring methodology

• Likelihood: 1 (rare) — 5 (almost certain)
• Impact: 1 (negligible) — 5 (critical)
• Risk score = Likelihood × Impact (1–25). Prioritize >12.

Core risk categories and template fields

For each risk row include: Risk ID, Category, Description, Likelihood, Impact, Score, Existing Controls, Required Controls, Owner, Target Date, Status.

Critical technical risk rows (fill for each product)

1. R1 — Local file system access
   • Description: Agent can read/write user files, DLP-sensitive directories, mounted network shares.
   • Existing Controls: OS ACLs, EDR visibility.
   • Required Controls: Scoped file access (explicit allow-list), runtime enforcement (sandbox or container), DLP integration to block exfiltration of classified data.
   • Detection: File access logs, EDR alerts, DLP triggers.
2. R2 — Secrets exposure (API keys, tokens, credentials)
   • Description: Agent or its subprocesses may read local credentials (browser stored tokens, config files, SSH keys).
   • Controls: Prevent access to credential stores, restrict browser extension permissions, require vault integration for secrets retrieval via least privilege.
   • Mitigation: Key vaulting + ephemeral tokens, prevent credential caching by agent.
3. R3 — Network exfiltration and C2
   • Description: Agent initiates outbound connections to vendor endpoints or third parties; risk of data exfiltration or covert command channels.
   • Controls: Egress filtering (FQDN/IP allow-list), proxy with TLS inspection, per-app network segmentation, deny all by default.
   • Monitoring: Outbound flow logs, UEBA anomalies for unusual destinations or data volumes.
4. R4 — Model updates & supply chain
   • Description: Vendor updates models or binaries that change behavior; risk of introducing vulnerabilities or removing controls.
   • Controls: Signed updates, SBOM, vendor attestations, canary deployments, pre-production validation, disable auto-update for enterprise deployments.
5. R5 — Telemetry & privacy
   • Description: Telemetry may include content snippets, usage data, or PII sent to vendor endpoints.
   • Controls: Explicit data minimization, configurable telemetry levels, contractual limits on PII retention, documented DPIA.
6. R6 — Privilege escalation & lateral movement
   • Description: Agent vulnerabilities or poorly confined components could be used to escalate privileges or move across the network.
   • Controls: Least privilege execution, container/VM isolation, EDR with exploit prevention, host hardening, service account isolation.
7. R7 — Integration risk (3rd-party connectors)
   • Description: Connectors to SaaS (Google Drive, Slack, GitHub) increase attack surface and multiply data-sharing policies.
   • Controls: Per-connector approval, scope-limited OAuth, connector review checklist, token expiration policies.
8. R8 — Legal & Compliance (GDPR, HIPAA, EU AI Act)
   • Description: Processing of regulated personal data or use of high-risk AI systems may trigger legal obligations.
   • Controls: Data mapping, DPIA, processors agreement, data residency options, assess AI Act classification and mitigation measures.
9. R9 — Observability & forensics
   • Description: Lack of sufficient logging to support investigations or meet audit requirements.
   • Controls: Centralized logging (app-level, OS, network), retention policy, structured telemetry schema, vendor support for forensic data export.
10. R10 — User behavior & social engineering
    • Description: Agents generating plausible text/code can be used to craft attacks; users may authorize risky operations.
    • Controls: UX gating for sensitive ops, confirmation prompts, senior-review workflows for high-scope actions, security training focused on AI-powered social engineering.

Sample completed entry (Anthropic Cowork — illustrative)

Below is a condensed example to show how a row might look when evaluating a desktop agent that exposes local file access and cloud model calls.

• Risk ID: R1
• Category: Local file system access
• Description: Cowork reads/writes files in user profile to synthesize documents and perform auto-sorting.
• Likelihood: 4
• Impact: 4
• Score: 16 (high)
• Existing Controls: Windows ACLs, EDR visibility
• Required Controls: Per-directory allow-listing; run Cowork in restricted VM for pilot; DLP rules that block classified folders; vendor attestation that agent will not exfiltrate file content without consent.
• Owner: Endpoint security team
• Target Date: 30 days before pilot
• Status: Remediation required

Operational gating checklist for pilots

Before approving a pilot, require these gates:

• Scoped launch: Limited user group (e.g., 10–25 knowledge workers) and non-production endpoints.
• Network controls: Egress proxy with allow-list for vendor domains, TLS-inspection where policy allows.
• Endpoint containment: Virtual desktop, sandbox, or dedicated VM image with no access to critical network shares.
• Telemetry & logging: App-level logs forwarded to SIEM; retention for 90 days minimum for pilot.
• Data policy: Clear guidance to users about allowed data types; DLP blocking for regulated info.
• Contract clauses: SLA for security incidents, timetables for patches, and right-to-audit.
• Rollback plan: How to remove the agent and revoke tokens quickly.

Technical controls — configuration patterns (2026 best practices)

These are practical settings and controls security teams should require.

• Network: Per-app egress allow-list, IP FQDN filtering at proxy, DNS filtering, TLS-intercept where lawful, and telemetry sampling to detect anomalous flows.
• Endpoint: Run agent in constrained user context; prefer VM/VDI or application containerization; block agent from starting persistent services with system-level privileges.
• Identity: SSO-only access (SAML/OIDC), enforce conditional access (device posture), and require periodic reauthorization for connectors.
• Data protection: Integrate with enterprise DLP and secrets manager; require configurable telemetry that excludes PII by default.
• Update management: Disable automatic updates by default; require signed updates and vendor changelogs and allow staged rollout.
• Supply chain: Require SBOM, vulnerability disclosures, and third-party penetration test reports (or attestations).

Detection & incident response patterns

Design IR playbooks that recognize AI-specific signals.

1. Establish baseline behavior: monitor normal file access patterns and common outbound endpoints for the agent.
2. Detect anomalous data flows: large outbound transfers, connections to non-whitelisted domains, or repeated attempts to access credential stores.
3. Immediate containment steps: suspend agent process, isolate host, revoke tokens at IDP, revoke API keys used by agent, and disable connectors.
4. Forensic artifacts to collect: process trees, memory dump, app logs, network captures, and SBOM for installed binaries. See postmortem templates and incident comms for guidance on evidence collection and stakeholder comms.
5. Vendor notification: have a pre-agreed SLA for response and mitigation. Document disclosure channels and timelines in contracts.

Procurement and legal clauses to demand

Embed technical requirements into contracts to ensure accountability.

• Security certifications (SOC 2 Type II, ISO 27001) and their recent reports.
• SBOM and signed update mechanism.
• Right to audit and third-party assessment reports.
• Data processing agreement with clear retention and deletion commitments; options for data residency.
• Vulnerability disclosure program and patch SLAs (e.g., critical patches within X days).
• Insurance and indemnity clauses for data breaches tied to vendor negligence.

Metrics to evaluate during pilot

• Security metrics: number of DLP blocks, number of anomalous egress events, number of vulnerabilities found.
• Operational metrics: time to onboard, support tickets created, percent of blocked workflows.
• Productivity metrics: time saved on core tasks, accuracy of generated artifacts, user satisfaction scores.
• Privacy metrics: fraction of telemetry including PII, percentage of requests routed to cloud vs local.

2026 trends and future-proofing guidance

Look ahead as desktop AI agents evolve:

• Hybrid inference: Agents will increasingly support local inference to reduce cloud exposure — require proof of model provenance, and ensure updates are auditable. See edge vs cloud inference guidance for trade-offs.
• Vendor transparency: Expect wider use of SBOMs, model cards, and third-party audits. Make these mandatory in procurement.
• Regulation: The EU AI Act and national guidelines now emphasize governance for high-risk AI systems — classify your use cases and apply stricter controls accordingly.
• Zero Trust integration: Desktop agents must be evaluated like any other service: identity-first access, continuous device posture checks, and micro-segmentation.
• Automation of assessments: Use IaC and policy-as-code to embed allow-lists and telemetry rules into your deployment pipelines.

Quick-start mitigations — the five things to do now

1. Require SSO and conditional access for any desktop AI app.
2. Run agents in constrained VMs or sandboxed user sessions during pilots.
3. Apply strict egress allow-lists and proxy all traffic for inspection.
4. Integrate with DLP and secrets vaults to prevent credential leakage.
5. Mandate SBOMs and signed updates in procurement documents.

Checklist: Go/no-go decision grid

Use this simple decision grid after assessment.

• Risk score highest category >12? — Require additional mitigations before pilot.
• Does vendor provide SBOM and signed updates? — If no, restrict to isolated pilot only.
• Is telemetry configurable to exclude PII? — If no, disallow processing of regulated data.
• Can the app be auto-updated? — If yes, require staged deployment and a rollback mechanism (see OS update practices).
• Is there a documented incident SLA (response/patch) from vendor? — If no, negotiate or disqualify.

Practical example: policy snippet for endpoint management

Include this in your endpoint policy to control desktop AI agents during pilot:

"Desktop AI agents are permitted only on designated pilot VDI images. Agents require SSO, per-application network allow-lists, do not store persistent credentials, and must run under non-admin local accounts. Telemetry must be configurable and default to minimal. Any connectors that access regulated data require pre-approval from Data Protection and Security teams."

Final takeaways

Desktop AI agents can materially improve productivity — but they are also new vectors for data exposure, supply-chain risk, and endpoint compromise. Use a structured, technical risk assessment to make measurable, repeatable decisions. Prioritize containment and telemetry during pilots and hold vendors accountable via procurement clauses and technical acceptance tests.

Call to action

Download the editable CSV risk register and procurement checklist and run a 30-day pilot with the controls above. If you want a tailored assessment template for your environment, contact your security program lead or start a trial with a vendor that supports enterprise deployability, signed updates, and SBOMs. Make the decision measurable — score risks, require mitigations, and only graduate to wide deployment after passing your security gates.

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