Cybersecurity, Data Privacy & Responsible AI
Recognise workplace AI risks and apply approved practices for information handling, verification and escalation.
15 lessons. A practical outcome.
Open a lesson to see what your team will learn.
01The Workplace AI Risk Map
Identify risks involving data, inaccurate outputs, cyber threats and inappropriate decisions. Match each risk to a realistic workplace activity and responsible owner.
02Approved Tools & Company Policies
Locate the organisation's tool list and acceptable-use rules. Practise checking approval, account configuration and permitted tasks before using a system.
03Classifying Information Before Sharing
Distinguish public, internal, confidential and sensitive information using company definitions. Decide what can be used, transformed or kept out of an AI workflow.
04Personal Data & Data Minimisation
Identify unnecessary personal details in sample inputs. Practise using the least information needed and routing unclear cases to the relevant company specialist.
05Retention, Access & Service Settings
Identify questions about storage, account access, reuse and deletion. Learn to request authoritative configuration information rather than assuming every tool handles data alike.
06Confidentiality & Intellectual Property
Recognise customer secrets, proprietary material and rights questions. Check permissions and approved sources before uploading or distributing content.
07Phishing, Impersonation & Deepfakes
Examine suspicious messages and simulated audio or video requests. Verify identity through established channels, especially before transferring funds or sharing information.
08Untrusted Documents & Prompt Injection
Spot embedded instructions that ask an assistant to reveal information or take actions. Apply approval boundaries and report suspicious content through the company's process.
09Checking High-Impact Outputs
Identify outputs whose errors could materially affect people or operations. Require appropriate evidence, specialist review and documented approval.
10Bias & Fairness in Practice
Inspect fictional outputs for stereotypes, exclusions and unjustified comparisons. Ask whose perspective is missing and how the result should be reviewed.
11Transparency & Accountability
Decide when AI assistance should be disclosed under company rules. Assign a human owner and keep enough evidence to explain the process and result.
12Human Oversight & Escalation
Define when to stop, ask a supervisor or consult security, privacy or another specialist. Practise resisting pressure to bypass controls for speed.
13Responding to an AI-Related Incident
Work through an accidental-upload or incorrect-output scenario. Follow the internal response route, record relevant facts and avoid further unauthorised disclosure.
14Build a Safe-Use Playbook
Create a concise checklist for approved tools, permitted inputs, verification, approval and incident reporting. Have the responsible company owners review it.
15Final Challenge: Handle Five Risk Scenarios
Respond to a suspected deepfake, confidential upload, unsupported claim, biased recommendation and malicious document. Explain the decision, safe next step and escalation route for each.
Leave with something
you can use.
- Role-specific safe-use checklist
- risk register
- escalation map
- scenario response record
What the review looks for
The final challenge brings the course together through practical work and review.
A path built around progress.
- 01
Lesson
Understand the concept.
- 02
Exercise
Practise with guidance.
- 03
Task
Apply it yourself.
- 04
Review
Reflect and improve.
Practical skills.
A certificate to show it.
Complete the assigned learning, submit the practical challenge and meet the agreed review requirements. Recognise that achievement with a certificate of completion.
Your programme brief confirms the requirements, reviewer and certificate delivery before you begin.
About certificatesCERTIFICATE OF COMPLETION
Learning worth
recognising.
AI Literacy for Everyone
Learner name · Completion date · Course title
AI Tutor