Data privacy
What happens to the prompts, files and personal data you put in.
Before you paste client data into an AI tool, you should know how it handles that data and its risks. SafeScore turns each tool’s privacy policy, security certifications and AI policies into one number from 0 to 100, with the evidence for every point.
SynthesiaStrongLittle public evidence of risk controls. Review carefully before sharing sensitive data.
Some important controls are missing or not documented.
Solid basics; a few controls are missing or unclear.
Most risk controls are documented in public.
Tools with too little public information to judge fairly show no shield (“not rated”) instead of a low score.
Every tool is reviewed against the same internal checklist, built on the AI risk frameworks companies already use, so two tools with the same public evidence get the same score.
What happens to the prompts, files and personal data you put in.
Independent security audits, encryption and enterprise controls.
Who is behind the tool, which AI models it uses, and what its limits are.
Published AI policies, risk frameworks, human oversight and incident reporting.
Protection against harmful content, misuse, deepfakes and copyright problems.
These are the dangers AI risk management frameworks focus on, what can go wrong, and what to look for.
Prompts, files and customer data you paste in can be kept and used to train future models, and may resurface in other people’s answers.
What to check: Look for a “we don’t train on your data” promise or an opt-out, and check it applies to your plan.
AI tools store conversations and uploads. A breach or a misconfigured share link can expose confidential client or company information.
What to check: Ask for SOC 2 or ISO/IEC 27001, encryption at rest and in transit, and SSO with access roles.
Language models state wrong facts, made-up citations and invented numbers with full confidence.
What to check: Prefer tools that cite sources, explain their limits and let a person review output before it is used.
Models can treat people differently by gender, age, ethnicity or language — a serious problem in hiring, lending, insurance and healthcare.
What to check: Check for a responsible AI policy, bias testing and human review for decisions about people.
Hidden instructions in web pages, emails or files can hijack AI agents to leak data or take actions you didn’t ask for.
What to check: Limit what agents can access, require approval before they act, and look for a security page and bug bounty.
Generated text, images, code and music can copy protected work, and ownership of the output may be unclear.
What to check: Read who owns the output, and look for IP indemnity on business plans.
Voice cloning and face-swap tools can be used to impersonate people for fraud or harassment.
What to check: Look for consent checks, watermarking or C2PA content credentials.
People stop checking AI output once it is usually right — until the one time it isn’t.
What to check: Keep a human in the loop for anything sent to customers, filed, published or paid.
The EU AI Act, GDPR, CCPA and sector rules (HIPAA, financial regulators) put duties on the companies that use AI, not only on the makers.
What to check: Check for GDPR/CCPA rights, a DPA, and the vendor’s work towards EU AI Act and ISO/IEC 42001.
Staff sign up for unknown tools with company data. If the company behind a tool is unclear, so is who is responsible when something goes wrong.
What to check: Keep a list of approved tools, and check the company, terms and contact details before anyone uploads data.
A practical seven-step process for small businesses and professional firms, in line with NIST AI RMF and ISO/IEC 42001.
SafeScore summarises public information and is not a certification, audit or legal advice. Always review the terms for the plan you use. Spotted a mistake? Tell us.