Review the evidence before you decide.
Security, privacy and responsible AI belong in your evaluation from the start. Use this guide to define the review for the Zolio products and data your organisation plans to use.
Start with the scope
A review for your organisation.
Bring the products you want to evaluate, the types of data involved and your security questionnaire. Include your HR, IT, privacy or procurement reviewers where relevant.
The request opens an enquiry with your security-review interest included. Agree the relevant material, review owners and next steps with the Zolio team.
See the enterprise evaluation guideSecurity review
Turn requirements into specific questions.
- Identity and access
Who can view candidate, employee and knowledge records? Which sign-in, permission and administrator controls apply to the products in scope?
Evidence to request: Ask for an access-control walkthrough and the supported identity options for your environment.
- Data protection
Where will data be hosted and processed? How are records separated, protected, retained and deleted?
Evidence to request: Request the relevant architecture, retention and data-handling information, including any subprocessors and transfers.
- Operations and incident handling
How are service incidents, access reviews, backups and recovery handled? Who will your team contact?
Evidence to request: Review operational responsibilities and any service, support or incident terms proposed for your engagement.
- Assurance and scope
Which assurance materials apply to Zolio, the proposed product and its hosting environment?
Evidence to request: Request current evidence with its legal entity, scope and date. Evaluate provider certifications separately from Zolio-specific assurance.
Responsible AI
AI supports evaluation.People remain accountable.
Zolio is decision-support software. Your organisation makes the final hiring, staffing or transition decision. Review how the proposed workflow supports that responsibility.
01
Evaluation criteria
Use a role or assessment relevant to your organisation. Review what is scored, which information is used and the limits of the output.
02
Supporting evidence
Ask to see how an evaluation connects to interview responses, assessment results or other source material. Identify where evidence is limited.
03
Human review
Agree who checks the output, handles a disputed result and makes the final decision. AI-supported evaluation should inform that decision.
04
Fairness and monitoring
Ask for the available testing approach, known limitations and a process for reviewing outcomes in your own context. No AI assessment should be treated as a guarantee of fairness.
Privacy and data use
Include the whole data journey.
Hiring records, student assessments, employee skills and captured role knowledge can raise different privacy questions. Review purpose, access, retention and any AI-model use for the specific data in scope.
Make the acceptance criteria explicit.
Before an engagement, record the products and deployment in scope, the information reviewed, outstanding requirements and the responsibilities agreed by both teams.
The proposed agreement is where service levels, data-handling terms and commercial commitments should be reviewed.
Read the website termsBring your security requirements.
Request a review of the information relevant to your evaluation. Avoid including candidate, employee or confidential records in the enquiry form.