EnterpriseBrowserSecurity

Switching From Chrome to a Local-AI Browser: Migration Checklist for Enterprises

ttechnique

2026-01-28

11 min read

A practical migration checklist for enterprises moving from Chrome to Puma-style local-AI browsers, covering security, endpoint management, policy, and training.

Why enterprises are rethinking Chrome in 2026 — and why Puma-style local-AI browsers matter

Hook: If your security, privacy, or productivity teams are frustrated by cloud-first browser AI, slow SSO flows, and opaque telemetry, you’re not alone. In 2026 many organizations are evaluating local-AI browsers (Puma-style browsers that run models on-device) to cut latency, reduce cloud data exposure, and enable new productivity workflows — but migration isn’t just a flip of a switch. This checklist gives CISOs, endpoint engineers, and IT training leads the practical, prioritized steps to move from Chrome to a local-AI browser safely and with measurable impact.

Executive summary — what to decide first

Most migrations fail or stall because senior stakeholders don’t align on three core decisions:

Scope: which user groups and OSes (Windows, macOS, iOS, Android, Linux, managed endpoints, kiosks)
Security posture: acceptance of local model execution vs. hybrid cloud inference
Success metrics: performance, privacy incidents reduced, productivity KPIs, and TCO

Quick recommendation: Start with a 3-month pilot for 100–500 technical users (developers, product teams, security engineers). Those users tolerate change and will surface integration gaps fast.

2026 context you need to know

Late 2025 and early 2026 accelerated several trends that make local-AI browsers viable for enterprises:

Wider availability of quantized on-device models and compact LLMs optimized for mobile/edge, reducing memory and power requirements.
Hardware NPUs/accelerators in mainstream laptops and phones (Apple, Qualcomm, Intel integrated NPUs) and broader support via WebNN and WebGPU for browser-accelerated inference.
Stronger privacy regulation and corporate data governance pushes to minimize cloud egress for sensitive browsing and research tasks.
Emergence of browsers like Puma that prioritize local inference and selective cloud fallbacks, with growing developer communities.

These developments change tradeoffs: lower latency, fewer third-party data flows, but new demands on endpoint management and policy design.

Migration checklist — high-level phases

This checklist is organized by phase. Each item includes the why, recommended action, and owner role.

Phase 0 — Pre-flight: governance and strategy (2–4 weeks)

Stakeholder alignment (Why): Migration impacts security, legal, identity, and end users. Action: Form a steering group (CISO, Head of IT, App Owners, Privacy Officer). Owner: IT Program Manager.
Define acceptable AI data flows (Why): Local inference still uses context—determine what can be processed locally vs. what must never leave device. Action: Create a data-class mapping (public, internal, restricted, confidential). For 'confidential' classify local-only by policy unless encrypted and consented. Owner: Privacy/Legal.
Risk assessment & compliance review (Why): Different browsers surface different telemetry and extension models. Action: Run a short risk workshop and update the enterprise browser policy to include local-AI specifics. Owner: Security Architect.
Vendor due diligence (Why): Evaluate Puma-style vendors for update cadence, enterprise support, and security posture. Action: Request SOC2/ISO docs, data flow diagrams, and SBC (software bill of materials). Owner: Procurement + Security.

Phase 1 — Security baseline and architecture (3–6 weeks)

Threat model for local inference (Why): Local models change attack surfaces: supply chain for model files, model poisoning, and local data exfiltration. Action: Add local model file verification (signed model artifacts), disk encryption check, and runtime integrity checks into the architecture. Owner: SecOps.
Endpoint hardening (Why): Device-level controls ensure local models don’t leak data. Action: Enforce full-disk encryption, enable OS-level privacy controls, and ensure antivirus/EDR signatures include the browser and model runtime. Owner: Endpoint Security.
Network policy & conditional access (Why): Local browsers may still access cloud services. Action: Update SASE and firewall rules to allow/block model update endpoints, and configure conditional access policies for browser SSO tokens. Owner: Network/Security.
Credential and secrets handling (Why): Browser-managed tokens and local LLM caches must be protected. Action: Implement OS-level keychain use, disable plaintext token caches; require FIDO2/WebAuthn for high-privilege accounts. Owner: Identity.

Phase 2 — Endpoint management & packaging (2–6 weeks)

MDM/UEM integration (Why): You must manage installs, updates, and policies. Action: Confirm the browser supports your MDM (Intune, JAMF, Workspace ONE). Package the browser for silent deployment; create configuration profiles that set enterprise defaults (home page, disable telemetry if supported). Owner: Endpoint Engineering.
Policy mapping from Chrome (Why): Enterprises often have many Chrome policies — map the essential ones. Action: Inventory Chrome policies in use (extensions, site allowlists, proxy settings) and produce a compatibility matrix: Supported / Partially supported / Not supported. Where feature parity is missing, document compensating controls. Owner: Browser Admin.
Auto-update and patching (Why): Local-AI browsers must keep models and binaries patched. Action: Configure auto-update policies for the browser and for on-device models. If the vendor exposes a model repository, whitelist update URLs. Create a quick rollback plan for model updates. Owner: Patch Management.
EDR and runtime policy (Why): Local model runtimes may look like suspicious processes. Action: Add allow-list rules and telemetry tags for the model runtime so analysts can distinguish expected behavior. Owner: SecOps/EDR Team.

Phase 3 — Compatibility testing & app integration (3–8 weeks)

Web app and extension audit (Why): Some internal web apps rely on Chrome-specific features or extensions. Action: Run a compatibility scan on critical internal apps. Test SSO flows (SAML/OIDC), file uploads, PDF handling, and WebAuthn. Identify features that require a fallback or short-term retention of Chrome for some groups.
Extension strategy (Why): Puma-style browsers may support a different extension model. Action: Inventory and prioritize extensions by usage. Migrate to vendor-supported equivalents or replace with native web app functionality. Consider using managed extension whitelists where supported.
Performance & model behavior tests (Why): Local LLM assists should be predictable and performant. Action: Measure latency and resource use on representative devices. Test model prompts for hallucination risk on internal data. Log and review outputs to tune model choice and prompt engineering.

Phase 4 — Pilot deployment (4–12 weeks)

Select pilot cohort (Why): Early adopters surface both UX and integration issues fast. Action: Choose 100–500 users across IT, Dev, Security, and Knowledge Work. Provide dedicated support channels and SLAs. Collect baseline productivity metrics (task completion times, support tickets).
Telemetry & monitoring (Why): You need signals to decide scale. Action: Enable privacy-respecting telemetry: browser health, crash rates, model update status, and number of local AI queries. Feed these into your observability stack (SIEM, APM). Owner: SecOps & SRE.
Incident playbooks (Why): Local models change incident patterns. Action: Update IR playbooks for model compromise, runaway local inference, and data exfiltration via browser APIs. Run tabletop exercises during the pilot.

Phase 5 — Rollout, training, and change management (ongoing)

User training (Why): Users need to understand model locality, privacy implications, and new features. Action: Deliver 30–60 minute hands-on sessions and short how-to videos explaining: local vs. cloud AI, how to clear caches, how to control model updates, and how to flag suspicious outputs. Provide quick reference cards for SSO and extension differences. Owner: IT Training.
Support model (Why): First-line support must troubleshoot both browser and model problems. Action: Create a triage flow: browser-only issues to IT helpdesk, model output concerns to security/privacy, and integration issues to app owners. Add troubleshooting scripts for common issues (e.g., model not loading due to disk quotas, network blocking of update endpoints).
Communication & change metrics (Why): Track adoption and sentiment. Action: Weekly rollout telemetry dashboards: install rate, active users, helpdesk tickets, and a simple NPS-style question about AI usefulness. Use feedback to adjust policies and training.

Privacy and policy specifics

Local-first does not mean risk-free. You must still define policies for model updates, caching, and telemetry.

Model provenance policy: Only run signed, vendor-verified models. Maintain a model SBOM and require cryptographic signatures for model artifacts.
Data residency & egress policy: Explicitly state what content can be summarized locally and what must not be used as prompt context. For regulated data (PHI, PCI), disallow automated local summarization unless approved and logged.
Telemetry minimization: Prefer opt-in for sensitive telemetry. Require vendor transparency on what telemetry they collect and provide a mechanism to disable non-essential telemetry via MDM.
Retention & audit trails: Log model update events, admin policy changes, and high-risk AI requests. Keep auditable records to support compliance.

Compatibility & fallback strategies

Complete parity with Chrome rarely exists. Define fallback strategies early.

Dual-browser policy: Allow Chrome for defined business-critical apps, while encouraging the local-AI browser for daily tasks. Explicitly document which teams use which browser and why.
VDI/managed sessions: For legacy or high-risk apps, use VDI/browser isolation strategies so you can centralize access to Chrome-managed sessions.
Short-lived exceptions: Implement exception workflows in your ITSM system so app owners can request browser-specific allowances with automatic review/expiration.

Operational best practices and hard-won tips (from 2026 pilots)

Start with the developers — they tolerated rough edges and fixed many missing integrations quickly.
Quantize model updates — ship model updates during maintenance windows and allow admins to pin a stable model for critical teams.
Label model outputs — require UI affordances that flag whether an answer was produced by a local model, which model version, and whether web context was accessed.
Test battery life and thermals — on-device inference can raise heat on laptops; include device health telemetry in pilot metrics.
Automate rollback — have a mechanism to force clients back to a known-good browser/version and to clear model caches centrally if a compromise is suspected.

Example checklist you can copy (actionable)

Inventory: list OS versions, Chrome policies in use, extensions, web apps, and core SSO flows. (Owner: Browser Admin)
Governance: update browser & AI policy, approve data-class mapping. (Owner: Privacy)
Vendor: obtain SOC2/ISO certificates, SBOM, and update endpoints list. (Owner: Procurement)
MDM packaging: build installers, config profiles, and auto-update policy. (Owner: Endpoint Eng)
Security: enable disk encryption, keychain usage, and EDR allow-listing. (Owner: SecOps)
Compatibility tests: SSO, WebAuthn, internal apps, and extensions. (Owner: App Owners)
Pilot: deploy to 100–500 users; enable telemetry and dedicated support. (Owner: IT Ops)
Training: create 1-hour hands-on session + 3 micro-videos. (Owner: IT Training)
Measure: weekly dashboard with install rate, tickets, latency, and user sentiment. (Owner: Project Manager)
Rollout: staged rollout by org unit with exception policy for legacy apps. (Owner: IT Program Manager)

How to measure success — KPIs for your business case

Security KPIs: reduction in cloud-data egress for browsing tasks, number of browser-related incidents, mean time to remediate.
Productivity KPIs: time saved per knowledge worker using local-AI assist features (measured with task timers), decrease in app-switching.
Operational KPIs: deployment rate, percentage of users on managed browser, ticket volume delta vs. baseline.
Cost KPIs: license and bandwidth cost delta, endpoint compute cost (battery/heat) where measurable.

Final recommendations and future-proofing

By 2026, local-AI browsers are no longer niche experiments — they're a practical tool in the enterprise toolbox. But successful migration requires treating the browser as part of an extended endpoint platform (model runtime, update service, telemetry, and identity). Prioritize:

Governance-first decisions — clear policies prevent surprise exposure.
MDM-managed deployments — automation reduces support load.
Pilot with technical users — they accelerate fixes and provide honest feedback.
Rigorous telemetry & rollback plans — you must be able to revert quickly if a model or update causes problems.

Practical takeaway: Treat the browser migration as a multi-year platform change, not a cosmetic swap. The most important asset you’ll manage is the trust that users and regulators place in your data handling.

Checklist one-page cheat sheet (printable)

Form steering group — 1 week
Data-class mapping & policy update — 2 weeks
Vendor diligence & SBOM — 1 week
MDM packaging & policy mapping — 2–4 weeks
Compatibility tests & EDR rules — 3–6 weeks
Pilot (100–500 users) with telemetry — 6–12 weeks
Staged rollout + training + support — ongoing

Closing — next steps for your team

Local-AI browsers like Puma bring real benefits in 2026: lower latency, reduced cloud egress, and new on-device productivity features. But migration touches governance, endpoint management, and user behavior. Use this checklist to run a focused pilot that proves security, compatibility, and user value before broad rollout.

Call to action: Ready to pilot a local-AI browser? Start by running an inventory of Chrome policies and extensions this week — it’s the one task that shortens your migration timeline the most. If you want a template, download our free migration inventory spreadsheet and pilot playbook (includes audit your tool stack, telemetry dashboards and training scripts) from technique.top/resources.

technique

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Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.