See how real hazards are named, rated before and after controls, and assigned to an owner. Each assessment is built around a vaayaa course, so you learn the method in the field you are studying.
Choose the field closest to your course.
Every page follows the same simple pattern.
Each hazard describes what could go wrong and who could be affected.
Likelihood times impact gives a score from 1 to 25, first before controls and then after.
Existing and additional controls explain how the score comes down.
A named role owns each hazard and a trigger says when to look again.
A worked example of how a professional kitchen identifies, scores and controls the risks that threaten guests, staff, food safety, service quality and the business, from fire and burns to allergens, fatigue and supplier gaps.
A worked example of how a customer service team finds, scores and controls the risks that threaten service quality, customer data, trust and continuity of support across phone, email, chat and self-service channels.
A worked example of how a security operations team finds, scores and controls risks to the confidentiality, integrity and availability of information, from identity compromise and supplier exposure to logging gaps and slow recovery.
A worked example of how an analytics team finds, scores and controls the risks in a data project, from poor data quality and analytical errors to privacy, bias, access, supplier licensing and model drift.
A worked example of how a data engineering team finds, scores and controls the risks that threaten pipelines, data stores and the people whose information they hold, from access misuse and misconfiguration to outages and privacy duties.
A worked example of how an analytics team finds, scores and controls risks in data quality, privacy, reproducibility, model validity and third-party data use, before results shape business decisions.
A worked example of how a visualization practitioner identifies, scores and controls risks that affect the accuracy, accessibility, privacy and delivery of charts and dashboards, from unclear briefs and misleading design to data exposure and wrong versions.
A worked example of how a database and data engineering team identifies, scores and controls the risks that threaten the confidentiality, integrity and availability of data, from credential compromise and misconfiguration to backup failure and privacy duties.
A worked example of how a team building and serving deep learning models finds, scores and controls risks to data, training pipelines, deployed models and the people who depend on them.
A worked example of how a general dental practice identifies, scores and controls risks to patient safety, infection control, consent, records, equipment and staffing, with owners, warning indicators and review triggers.
A worked example of how a dermatology and aesthetic clinic finds, scores and controls the risks to patient safety, infection control, consent, records, product supply and staff competence that arise in consultations and minor procedures.
A worked example of how a student design team finds, scores and controls risks in visual work, from unclear briefs and unlicensed imagery to accessibility gaps, version confusion and file compatibility problems.
A worked example of how a design thinking and creative strategy team finds, scores and controls risks to briefs, private material, schedules, approvals, accessibility, ethics, licensing and production quality.
A worked example showing how a radiology service identifies, scores and controls risks to patients, staff and imaging data, from wrong-site procedures and contrast reactions to radiation dose, privacy, equipment outages and delayed urgent results.
A worked example of how a digital banking and fintech team finds, scores and controls the risks that threaten customer money, personal data, lending limits and reporting duties, from payment errors and fraud to cyber exposure and supplier dependency.
A worked example of how a small creative studio finds, scores and controls the risks behind short-form and long-form social video, from music rights and releases to platform dependence, filming safety and online abuse.
A worked example of how a digital forensics and threat intelligence team finds, scores and controls risks to evidence integrity, analytic quality, legal boundaries and sensitive data.
A worked example of how a regional digital health service finds, scores and controls risks to records, clinical handover, supplier reliance, patient safety and financial integrity, with owners and review triggers for each.
A worked example of how a marketing team finds, scores and controls the risks in digital and online advertising, from misleading claims and privacy breaches to fake traffic, account takeover and platform dependency.
A worked example of how a small online business finds, scores and controls the risks in its digital payment flow, from transaction processing and reconciliation to fraud, access, cash and data exposure.
A worked example of how a small online retail marketing team finds, scores and controls the risks that threaten campaign results, legal compliance, customer data, brand trust and access to its sales channels.
A worked example of how a small eco wellness retreat finds, scores and controls risks to guest safety, food, bookings, data, supplies and its sustainability promises.
A worked example of how an editorial design team finds, scores and controls the risks to briefs, confidential material, approvals, printing quality, schedule, image rights and accessibility in a printed publication.
A worked example of how a learning and development team finds, scores and controls risks to learner wellbeing, fair assessment, confidential records, platform continuity and course compliance.
For Learning Reference Only: This assessment demonstrates how risk assessment principles are applied in a practical setting, providing a useful reference for learning and understanding. Actual assessments may be adapted to suit specific site conditions and circumstances.
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