Is My AI Tool Safe? 7 Things to Check Before You Use Any AI at Work

How to actually assess whether an AI tool is safe to use

The marketing description of an AI tool tells you almost nothing useful about its safety. Every vendor describes their tool as "secure," "ethical," "responsible," and "compliant." Most have published AI policies and transparency reports. Yet AI tools differ enormously in their actual safety properties — what they do with your data, what they were trained on, how they behave under adversarial inputs, and whether they will continue to operate predictably as the vendor updates them.

This article gives you a structured approach for assessing whether a specific AI tool is safe enough for your specific use. Safety here means: data protection (your inputs are not exposed to others); model behaviour (the tool does what it claims, predictably); intellectual property (you are not exposing yourself to copyright or training data issues); regulatory compliance (the tool's deployment fits your regulatory environment); and continuity (the tool will continue to operate predictably).

Step 1 — Identify your use case and risk level

The same tool may be safe for one use and unsafe for another. Before assessing the tool, define what you'll use it for: what data will you input (public information, internal documents, customer data, regulated data); what outputs will you rely on (drafts to be reviewed, decisions, customer-facing content); what consequences arise if outputs are wrong (minor inconvenience, regulatory breach, customer harm); what regulatory frameworks apply to your use (GDPR, sector-specific regulation).

Generally, AI use for: public information research and brainstorming is low risk; drafting internal content (emails, summaries, slides) for human review is medium risk; processing client or customer data is high risk; making or substantively informing decisions about people (hiring, credit, healthcare) is highest risk and typically triggers specific regulatory requirements.

Step 2 — Check the tier you're using

The same AI vendor often offers multiple tiers with materially different safety properties. ChatGPT free, ChatGPT Plus, ChatGPT Team, and ChatGPT Enterprise have different data handling. Claude.ai free, Claude Pro, Claude Team, Claude Enterprise differ similarly. Most consumer-tier AI tools train on your inputs by default (often disclosed but easy to miss). Most enterprise-tier tools do not train on customer inputs by default and offer additional security commitments.

For any business or regulated use, you typically need an enterprise tier or business-grade subscription with: training opt-out by default; data residency commitments where required by your regulatory environment; SOC 2 Type II or equivalent attestation; specific contractual terms (Data Processing Agreement, Business Associate Agreement for HIPAA, etc.).

Step 3 — Verify the vendor's substantive credentials

Look at what the vendor has published — not just claims, but verifiable evidence:

Security attestations. SOC 2 Type II is the minimum bar for any business use. ISO 27001 indicates structured information security management. ISO/IEC 42001 (the AI management system standard) is increasingly expected — vendors with ISO 42001 certification have undergone independent audit of their AI governance.

Transparency reports. Major AI vendors (OpenAI, Anthropic, Google, Microsoft) publish reports on model capabilities, limitations, safety testing, and usage. Substantive content here is a good sign; vague generalities are not.

Model documentation. Model cards, system cards, and technical documentation showing what the model was tested for and what known limitations exist. The depth and specificity here separates serious vendors from marketers.

Litigation and enforcement history. Search for major lawsuits or enforcement actions involving the vendor. Foundation model providers face significant copyright litigation (NYT v OpenAI commenced December 2023, similar cases ongoing). Concentration of litigation does not necessarily mean unsafe but creates downstream uncertainty.

Step 4 — Verify data handling specifically

The single most consequential safety question: what happens to your data? Specifically: is your data used to train the vendor's models? Is your data stored, and for how long? Who can access your data (vendor staff, sub-processors, government agencies)? Where is your data processed and stored (jurisdiction matters for regulatory compliance)?

For consumer-tier tools, the default is typically: your inputs may be used for training; data is stored for varying periods; vendor staff may review content; processing locations may vary globally. For enterprise tools, the default should be: no training on your data; data is not retained beyond session unless you configure storage; processing locations meet your jurisdictional requirements.

The Data Processing Agreement (DPA) is the document that captures these commitments contractually. If the vendor cannot provide a DPA you can review, that is itself a signal.

Step 5 — Test the tool's behaviour

Vendor claims about model behaviour mean less than your own testing. Before deploying for any consequential use, test the tool on representative tasks and data with: known correct answers (does the tool produce them?); ambiguous inputs (does the tool acknowledge uncertainty?); adversarial inputs (does the tool resist prompt injection or social engineering?); your specific edge cases (does the tool handle them gracefully or fail dangerously?).

For consequential AI use, this testing should be structured, documented, and repeated when the model is updated. "We tested it once and it worked" is not adequate for AI tools that may behave differently after vendor updates.

Step 6 — Check regulatory fit

Different uses trigger different regulatory requirements. For high-risk use cases under the EU AI Act (employment, credit, healthcare, education, biometrics), you need to confirm the vendor's classification under the AI Act, the existence of technical documentation, and conformity assessment evidence. For US use, NIST AI RMF alignment, FDA clearance for medical AI, HIPAA compliance for health data. For Australian financial services use, CPS 230-compliant contracts. For UK use, ICO guidance compliance and Articles 22A-D considerations where solely automated decisions are involved.

Step 7 — Plan for failure

No AI tool is safe in absolute terms. The right question is: when this tool fails or behaves unexpectedly, what happens? You need: a procedure for detecting failures (monitoring outputs, user feedback mechanisms); a process for responding to failures (rollback, fallback to manual processes, vendor escalation); an exit plan (data return, transition to alternative vendor, in-house capability development for critical use cases).

The vendor's safety commitments matter less than your operational ability to detect and respond when the tool fails.

The honest answer about "safety"

No AI tool is completely safe. The question is whether the tool is safe enough for your specific use given the risks you're willing to accept and the controls you have in place. A consumer chatbot for personal brainstorming may be perfectly safe. The same chatbot used to draft regulatory submissions or process customer data may not be. The same enterprise AI for low-risk drafting may be safe; the same tool for AI-driven hiring decisions may not be. Safety is contextual, and the assessment must be contextual to be useful.