Security Review Template for Desktop Autonomous AI Agents

Hook: Stop saying "yes" to desktop-level permissions — start a repeatable security review

Every week a new vendor asks for desktop-level permissions: file system access, keychain or credential store access, global hotkeys, or the ability to spawn processes. For busy engineering and security teams this creates a costly manual loop — research, meetings, ad-hoc mitigations, and weeks of delay. In 2026 the problem is worse: desktop autonomous agents (from research previews like Anthropic’s Cowork to embedded assistant features in consumer devices) expect wide permissions to deliver value. You need a concise, standardized security review template to evaluate these requests quickly and defensibly.

Why this matters in 2026

Desktop autonomous agents are now mainstream. Vendors ship agents that read your documents, synthesize spreadsheets, and act on behalf of users. Strategic partnerships (for example, Apple integrating Google’s Gemini models into assistant features) and an accelerating wave of AI desktop apps mean administrator approvals are happening more often and under pressure. Meanwhile regulators and auditors have greater scrutiny on data flows and supply chain provenance — from the EU AI Act enforcement to strengthened guidance on software supply chain integrity.

Quick trend summary (late 2025–early 2026): autonomous desktop agents rise, privacy/supply-chain enforcement tightens, and organizations standardize assessments to scale approvals.

What this template delivers

This article provides a complete security review template tailored for vendors asking for desktop-level permissions. It includes:

• Standard sections to collect vendor responses (identity, provenance, permissions)
• A practical threat model and mapped mitigations
• Data flow and logging schemas you can copy into your intake system
• Concrete runtime hardening and observability checks
• A scoring rubric for go/no-go decisions

How to use this template

Use the sections below as a vendor intake form or checklist in your internal developer portal. Require vendors to fill each field and attach artifacts (SBOM, SLSA provenance, signing certificates, binary checksums). Use automated gates where possible (policy-as-code, attestation checks) and human review for high-risk items.

1) Executive summary (one-paragraph, vendor-provided)

Fields to collect:

• Purpose: One-line description of what the agent will do on the desktop.
• Minimum permissions requested: exact OS-level scopes (macOS Transparency, Windows UAC/Consent prompts, Linux capabilities).
• Data residency: where data is stored and processed (local only, cloud endpoints with region).
• Risk tier: vendor-assigned and vendor-justified (low/medium/high).

2) App identity, provenance & supply chain

Collect evidence the binary is what it claims and that the build pipeline is auditable.

• Signed binaries: signatures, signing certificate chain, and verification steps.
• SBOM: SPDX or CycloneDX file for the release.
• Checksums: SHA-256 for all release artifacts and installer packages.
• Build provenance (SLSA): SLSA level claimed and build link or attestation (provenance.json).
• Third-party ML models: model metadata (weights checksum, provider, licensing).

Provenance checklist (automatable)

1. Verify SLSA provenance artifact exists and matches the released checksum.
2. Validate SBOM against expected dependencies and flag known-vulnerable components (CVE list).
3. Confirm binaries are code-signed and the chain resolves to expected vendor CA.

3) Permissions & data flows (required diagram)

Ask vendors for a clear data-flow diagram and a permission matrix. Require a machine-readable JSON snippet describing the same flow so you can ingest it into your risk platform.

Sample data-flow JSON (minimal)

{
  "app":"example-agent",
  "permissions":[
    {"scope":"filesystem:read","paths":["/Users/*/Documents"],"justification":"index and summarize user docs"},
    {"scope":"network:outbound","endpoints":["https://api.vendor.ai"],"justification":"model inference"},
    {"scope":"clipboard","justification":"paste generation results"}
  ],
  "data_flows":[
    {"from":"/Users/alice/Documents/report.docx","to":"/tmp/example-agent/processed","processing":"local parsing, then model upload (PII redaction)"},
    {"from":"/tmp/example-agent/processed","to":"https://api.vendor.ai/v1/infer","fields":["text","metadata"]}
  ],
  "persisted":{
    "local":"/Library/Application Support/example-agent/cache","cloud":"vendor:s3://example-agent/outputs (eu-central-1)"
  }
}

Use this JSON to detect excessive scopes (e.g., app requesting filesystem:read for the entire root) and to automatically map network endpoints to allowed CIDR ranges and regions. If you automate ingestion, tie parsing to your composable pipelines so pre-deploy gates can validate hostnames and regions.

4) Threat model — prioritized by impact

Use a compact, repeatable threat matrix. For desktop agents the common attack classes in 2026 are:

• Local data exfiltration: agent reads sensitive files and uploads them to an external endpoint.
• Credential theft: agent accesses system keychain/credential stores.
• Privilege escalation: agent spawns processes or loads native libraries to escalate.
• Supply-chain compromise: malicious dependency or model weights.
• Abuse of automation: agent executes actions (send email, transfer funds) on behalf of users.

Threat → Minimal acceptance criteria (examples)

• Local data exfiltration: vendor must demonstrate PII redaction, explicit per-file consent UX, and enforce domain allowlist for outbound requests.
• Credential theft: vendor must never request raw keychain/private-key read; instead support secure OS-managed prompts and tokenized access with time-bound tokens.
• Privilege escalation: agent must run unprivileged by default and require OS-level user consent for elevated actions; provide deterministic sandboxing options.
• Supply-chain compromise: vendor must provide SBOM, SLSA provenance, and attestations for model weights with checksums.
• Abuse of automation: require human-in-the-loop confirmations for sensitive actions and record approved prompts in audit logs.

5) Mapped mitigations and runtime hardening

For each threat, require concrete mitigations. Here are practical, enforceable controls:

• Least privilege: restrict filesystem paths and network endpoints. Implement path allowlists and deny-by-default networking.
• Hardware-backed keys: use TPM/Secure Enclave for local secrets and attestation where possible.
• Local-only processing option: vendor must offer a configuration that keeps inference on-device for sensitive datasets.
• Signed policies & policy-as-code: enforce allowed actions via OPA/Rego or equivalent policy agents.
• Rate limiting & quotas: prevent mass exfiltration by limiting outbound uploads and throttling heavy reads.
• Process isolation: run untrusted model execution in sandboxed processes (macOS App Sandbox, Windows AppContainer, containerized runtimes).

Example OPA/Rego rule (deny full-root FS read)

package desktop.agent

# Deny if requested paths include the root directory
deny[msg] {
  input.permission == "filesystem:read"
  path := input.paths[_]
  startswith(path, "/")
  path == "/"
  msg = "Full root filesystem access is denied"
}

6) Logging, observability & alerting (required schema)

Logging is the number-one tool for proving safe behavior and diagnosing incidents. Require vendors to emit structured logs and to support push or pull ingestion into your SIEM. Minimum logging requirements:

• Event types: permission_grant, file_read, file_write, network_request, process_spawn, user_consent, error, model_inference
• Fields: timestamp (ISO8601), app_id, device_id, user_id (hashed if PII), event_type, resource_path, network_destination, result (success/failure), request_id
• Outbound telemetry: allowlist hostnames and TLS cert pinning for telemetry endpoints; no plaintext PII in logs.
• Retention & export: logs retained for at least 90 days by default; support automated export to customer SIEM (syslog/HTTPS).

Sample structured log entry (JSON lines)

{
  "timestamp":"2026-01-15T12:21:34Z",
  "app_id":"example-agent",
  "device_id":"host-1234",
  "user_id":"sha256:abc...",
  "event_type":"file_read",
  "resource_path":"/Users/alice/Documents/report.docx",
  "result":"success",
  "request_id":"req-9876"
}

Require vendors to provide a test stream and a schema file so you can onboard ingestion automatically. Add SIEM detection rules for anomalies like sudden spikes in file_read events or outbound network_request events to new hosts. For real-time monitoring guidance and observability patterns, see operational playbooks on hybrid monitoring and low-latency observability.

7) Detection rules and example SIEM queries

Start with a small set of high-fidelity rules to reduce alert fatigue:

• High-volume file reads: alert when file_read > 200 files/minute for any single user.
• Unapproved network destinations: alert on network_request to domains not in allowlist.
• Credential store access: alert on any attempt to access system keychain or credential manager.
• Unexpected process spawn: alert when the agent spawns binaries not in signed-binary allowlist.

Example pseudocode detection rule

if count(file_read where user_id == X and timestamp > last_1_min) > 200:
  trigger_alert("High-volume file reads by user X")

if network_request.destination not in allowlist:
  trigger_alert("Outbound to unapproved host")

8) Incident response & playbooks

Vendor must provide an incident response plan and contact details. Required commitments:

• 90-minute initial acknowledgement SLA for critical incidents.
• Forensic packages: vendor supplies logs, SBOM, and binary artifacts for a specified retention window.
• Revokeability: vendor supports remote revocation of agent access tokens and provides instructions for rapid IIS/OS-level removal.

9) Privacy, data handling & consent UX

Clear UX and auditability are crucial. Require:

• Per-file consent dialogs with clear machine-readable consent tokens.
• Option to disable cloud processing for sensitive projects.
• Data minimization: only fields required for inference are transmitted.
• Privacy DPIA or equivalent when processing personal data in EU/UK.

10) Compliance evidence & certifications

Ask for the following artifacts (where applicable):

• SOC 2 Type II report or equivalent
• GDPR DPIA and retention policies
• SLSA provenance, SBOM
• Attestation for model weights and training data provenance (for regulated uses)

11) Scoring rubric: how to make a go/no-go decision

Score each major area 0–3 (0 = unacceptable, 3 = best-in-class). Example areas:

• Provenance & SBOM (0–3)
• Least-privilege enforcement (0–3)
• Logging & observability (0–3)
• Incident response & SLAs (0–3)
• Privacy & compliance artifacts (0–3)

Suggested decision policy:

• Total >= 12: Green — allow with standard monitoring
• Total 8–11: Amber — allow with mitigations and limited pilot
• Total < 8: Red — deny until remediation

12) Example completed vendor response (abridged)

{
  "app":"example-agent",
  "purpose":"Summarize user-selected documents into briefings",
  "permissions":[{"scope":"filesystem:read","paths":["/Users/*/Documents/Work"],"justification":"summarization"}],
  "sbom":"https://vendor.s3/sbom/example-agent.spdx.json",
  "slsa_provenance":"https://vendor.s3/provenance/example-agent.slsa.json",
  "logging_endpoint":"https://ingest.customer-siem/collector",
  "incident_sla":"90m",
  "local_processing_option":true
}

Advanced strategies and 2026 best practices

As desktop agents mature, treat them like microservices with observable contracts. Advanced teams in 2026 adopt:

• Policy-as-code gates: automated pre-deploy checks against vendor-provided data-flow JSON.
• Hardware attestation: require vendor binaries to be verifiable via TEE-backed attestation for high-risk deployments.
• Zero trust for apps: per-app network proxies and mTLS to limit communication to vendor-specified endpoints.
• Model provenance audits: for agents using 3rd-party models, require provenance and licensing checks to avoid IP and compliance exposure.

Quick wins for engineering and ops

• Automate intake: turn the data-flow JSON into a verification pipeline that checks hostnames, regions, and path scopes.
• Preset policies: maintain pre-approved profiles for low-risk agents (e.g., read-only in a single documents folder).
• Sandbox templates: provide vendor sandbox configurations (macOS App Sandbox entitlements, Windows AppContainer manifests) to speed reviews.

Actionable checklist (one-page)

• Require SBOM + SLSA provenance
• Collect data-flow JSON and permission matrix
• Validate binary signatures and checksums
• Require structured logs and SIEM onboarding test stream
• Confirm IR SLA and revocation capability
• Score vendor and decide via rubric

Closing: scale reviews without sacrificing security

In 2026 desktop autonomous agents are neither rare nor niche. They are a standard productivity surface and thus will appear in procurement conversations often. By using a standardized security review template — one that demands provenance artifacts, maps data flows, enforces least-privilege, and requires structured observability — you convert ad-hoc requests into predictable, auditable approvals. This reduces manual work for engineering, shortens procurement cycles, and keeps your organization secure.

Takeaways

• Demand machine-readable artifacts: SBOM, SLSA, and a data-flow JSON make automation possible.
• Log everything meaningful: structured events enable detection and forensic work.
• Enforce least privilege: path and network allowlists are non-negotiable.
• Score vendors consistently: use a rubric to remove subjectivity.

Call to action

If you’re evaluating desktop agents this quarter, download the ready-to-use JSON schema, Rego policies, and SIEM detection templates we use internally. Implement the intake pipeline and cut average review time from weeks to hours. Contact our team or visit flowqbot.com/resources to get the template bundle and a short onboarding guide tailored for engineering and security teams.

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