Security, reliability and data risks for IT, software, network and systems courses.
A worked example of how a precision farming engineering team finds, scores and controls risks from machinery guidance, sensor data, spraying, electrical systems and supplier dependence, with inherent and residual ratings.
A worked example of how a team building an AI-enabled solution identifies, scores and controls the risks to objectives, data, testing, people, suppliers and users, from scope creep to harmful outputs.
A worked example of how an engineering team finds, scores and controls risks in vehicle and aircraft component design, testing, assembly and supply, from structural failure and battery hazards to specification mismatch and record errors.
A worked example of how a learner builds a risk assessment for a small team running cloud services on Azure, covering identity, data, configuration, third parties, recovery and logging.
A worked example of how a biomedical engineering team finds, scores and controls risks from device faults, staffing gaps, infection, data privacy and system outages in patient care.
A worked example of how a blockchain development team finds, scores and controls risks to smart contracts, signing keys, infrastructure, user data and people, from code defects and phishing to third-party libraries and incident response.
A worked example of how a small team building blockchain and Web3 services finds, scores and controls risks to keys, smart contracts, personal data, third parties, uptime and incident response.
A worked example of how a media production team finds, scores and controls risks to technical delivery, audio quality, rights clearance, confidential material, schedules and approvals, with role-based owners and warning indicators.
A worked example of how a business intelligence development team finds, scores and controls the risks that affect pipelines, data quality, privacy, licensed data and the trust placed in dashboards and reports.
A worked example of how a business intelligence team identifies, scores and controls risks from licensed data, sampling bias, privacy gaps, pipeline failure and misleading dashboards, with owners and review triggers.
A worked example of how a teaching chemical processing unit identifies, scores and controls hazards, from reactor runaway and loss of containment to maintenance errors, utility loss, supplier quality and permit compliance.
A worked example of how a civil and structural engineering team finds, scores and controls the risks in design, temporary works, site operations, materials and maintenance of built assets.
A worked example of how a planning team finds, scores and controls the risks in a cloud platform design, from misconfigured storage and stolen credentials to outages, weak logging and privacy duties.
A worked example of how a cloud and DevOps team finds, scores and controls risks to identities, build pipelines, stored data, service availability and logging, with inherent and residual ratings and named owners.
A worked example of how a team running a camera-based vision system finds, scores and controls risks to accuracy, image privacy, access, service continuity and supplier dependence.
A worked example of how a construction site and its plant fleet identify, score and control risks from lifting and plant failure to services contact, procurement defects and fuel spills, with role owners and review triggers.
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 digital forensics and threat intelligence team finds, scores and controls risks to evidence integrity, analytic quality, legal boundaries and sensitive data.
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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