AI Privacy and Safety for SMEs: A Checklist

SMEs can gain the benefits of AI without courting risk by installing a lightweight governance layer that controls data, tools, people, and vendors; the checklist below prioritizes quick wins first, then deeper controls that scale with growth.

Map usage and risks

• Inventory every AI tool in use (including “shadow AI”), the data it touches, and the business process it affects; classify each use case as low, medium, or high risk based on sensitivity and impact.

• Document data flows for each use (inputs, prompts, outputs, storage, logs), noting any personal data, regulated data, or client-confidential information.

Set policy and guardrails

• Publish a 1–2 page AI Acceptable Use Policy: approved tools, prohibited uses (e.g., legal/medical advice, decisions about individuals), restricted data classes, review requirements, and escalation paths.

• Define a “safe vs restricted data” matrix for prompts; prohibit entering secrets, credentials, PCI, PHI, or client-confidential data into public models without written approval.

Configure secure defaults

• Turn on enterprise controls where available: data‑processing addenda, data‑use restrictions (no training on customer content), region pinning, retention limits, audit logging, and SSO/SAML.

• Enforce least‑privilege access to data sources that copilots can reach; remember assistants surface what users can access, not what they should access.

Handle personal data lawfully

• Identify lawful bases for any personal‑data processing in AI workflows; minimize collection, pseudonymize where possible, and respect data‑subject rights.

• Run a lightweight DPIA for moderate/high‑risk AI use cases; record risks, mitigations, and approvals.

Procurement and vendor diligence

• Require AI vendors to provide a security and privacy summary: data flows, training use of customer data, sub‑processors, retention, encryption, model/endpoint locations, and incident response.

• Add contract controls: DPA, breach notice, no training on your content by default, model/region transparency, and clear liability caps aligned to risk.

Human review and quality control

• Institute “human‑in‑the‑loop” review for external content, decisions that affect people, and anything compliance‑sensitive; create checklists for fact‑checking and citations.

• Watermark or label AI‑assisted content internally; maintain version history and who approved what.

Model and prompt hygiene

• Centralize approved prompts for repeat tasks; remove customer identifiers and secrets; prefer retrieval over pasting source data.

• Log prompts and outputs for key workflows; sample for accuracy, bias, and leakage each month.

Security integration

• Add AI to existing security policies: password managers, MDM on endpoints, DLP rules blocking uploads of restricted file types, and egress monitoring for prompt‑paste patterns.

• Train staff to recognize prompt‑injection, data‑exfiltration attempts, and malicious file outputs.

Bias, fairness, and safety checks

• For any AI output that impacts people (hiring, lending, support prioritization), define measurable fairness criteria and test on representative samples; keep test records.

• Provide a clear path for users and staff to report harmful or biased outcomes; triage and fix quickly.

Transparency to customers

• Update your privacy policy to explain AI use, categories of data processed, retention, and user choices; link to an “AI Use” page that lists high‑impact applications in plain language.

• Offer contact routes for questions, opt‑outs where feasible, and a service‑level for human review on request.

Training and culture

• Onboard every new hire with a 30‑minute AI safety briefing: what not to paste, approved tools, and review standards; refresh quarterly with new risks and examples.

• Reward teams for safe automation ideas; make it easy to request new AI tools through a simple intake form.

Record‑keeping and audit

• Maintain a single register of AI use cases, risk ratings, approvals, vendors, DPAs, and DPIAs; review quarterly.

• Tag projects that may fall under higher‑risk categories (hiring, credit, health) and pre‑plan extra controls.

Incident readiness

• Extend your incident response plan to cover AI: hallucinated defamation, data leaks via prompts, unsafe code suggestions, and harmful customer interactions; run a tabletop twice per year.

• Prepare takedown/rollback procedures and messaging templates for rapid correction.

Roadmap for SMEs (90 days)

• Weeks 1–2: Inventory tools and data; ship the 2‑page AI policy; enforce SSO and basic vendor DPAs.

• Weeks 3–4: Stand up prompt and output review for external content; deploy DLP rules; publish “AI Use” and privacy updates.

• Weeks 5–8: Run DPIAs on high‑risk cases; centralize approved prompts; add audit logging and retention limits.

• Weeks 9–12: Bias/fairness tests for people‑impacting use; tabletop incident drill; quarterly register review.

Minimal templates to copy

Policy outline

• Purpose and scope

• Approved tools and prohibited uses

• Data classification and handling rules

• Review and approval thresholds

• Incident reporting and escalation

DPIA one‑pager

• Use case and purpose

• Data categories and sources

• Risks (privacy, bias, security) and impact rating

• Mitigations and residual risk

• Owner, approver, review date

Vendor questionnaire (short)

• Data used for training? Default off toggles?

• Storage/retention controls and regions

• Sub‑processors and certifications

• Encryption in transit/at rest; key ownership

• Logging/audit access and deletion paths

Adopt the above as “small but strong” controls: brief policies, concrete defaults, and monthly hygiene checks will cover most SME risk while preserving the speed that makes AI valuable.