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6 courses · 22 modules

Pick-and-choose AI training

Six courses, 22 modules. Choose an individual course or build a custom series for your audience and objectives.

Shape your training

Explore the courses

ALL / 5 modules

General public

  • Getting started and usage guide
  • AI: more intelligent or more artificial?
  • How to protect your privacy
  • Known and unknown risks
  • Making AI useful in everyday life
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MGT / 4 modules

Agentic systems for managers

  • Balancing risks and opportunities
  • AI for you
  • Maximizing ROI
  • How to avoid catastrophe
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OPS / 4 modules

AI opportunities

  • Human resources
  • Accounting
  • Project management
  • Administration
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POL / 3 modules

Designing your internal AI policy

  • Balancing risks and opportunities
  • Preserving professional confidentiality
  • Data security and information leakage
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DEV / 3 modules

Agentic programming

  • Organizing work and avoiding rework
  • Isolation, sandboxing, permissions, and secrets management
  • When to read the code
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SEC / 3 modules

Cybersecurity in the age of agents

  • Confidential data
  • Data leaks through prompt injection
  • Proper agent isolation
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General public

5 modules

ALL_01

Getting started and usage guide

Objective

Formulate requests, refine instructions, and verify answers.

Topics covered

  • Requests and context
  • Instructions and iteration
  • Answer checking

Key learning outcomes

  • Ask a clear question.
  • Adapt instructions after a first response.
  • Check the important parts of an answer.

ALL_02

AI: more intelligent or more artificial?

Objective

Distinguish apparent intelligence, model capabilities, and their limits.

Topics covered

  • Apparent intelligence
  • Model capabilities
  • Limits of responses

Key learning outcomes

  • Distinguish a convincing answer from a reliable one.
  • Explain a model limitation using an example.

ALL_03

How to protect your privacy

Objective

Identify sensitive information and choose sharing practices and settings.

Topics covered

  • Sensitive information
  • Sharing practices
  • Privacy settings

Key learning outcomes

  • Identify data to leave out of a request.
  • Review settings before sharing information.

ALL_04

Known and unknown risks

Objective

Recognize documented failures, uncertainty, and situations where it is time to pause.

Topics covered

  • Documented failures
  • Uncertainty
  • Reasons to pause

Key learning outcomes

  • Spot a sign of failure in an answer.
  • Distinguish a known risk from uncertainty.
  • Explain when to pause a use.

ALL_05

Making AI useful in everyday life

Objective

Explore personal planning, learning, and writing without giving up verification.

Topics covered

  • Personal planning
  • Learning
  • Writing and verification

Key learning outcomes

  • Choose an everyday task to explore with AI.
  • Prepare a plan or draft suited to a need.
  • Check information before using it.
Discuss this course →

Agentic systems for managers

4 modules

MGT_01

Balancing risks and opportunities

Objective

Weigh value, autonomy, oversight, and exposure in agent use cases.

Topics covered

  • Value and exposure
  • Degrees of autonomy
  • Human oversight

Key learning outcomes

  • Compare a use case’s expected value and risks.
  • Propose an appropriate level of autonomy and oversight.

MGT_02

AI for you

Objective

Apply AI to a manager’s preparation, synthesis, and follow-up.

Topics covered

  • Management preparation
  • Information synthesis
  • Follow-up

Key learning outcomes

  • Prepare a useful meeting brief.
  • Structure follow-ups from available information.

MGT_03

Maximizing ROI

Objective

Assess costs, expected benefits, adoption, and measurement without assuming a guaranteed return.

Topics covered

  • Costs and expected benefits
  • Adoption
  • Return measurement

Key learning outcomes

  • Identify costs and benefit assumptions.
  • Choose indicators to track adoption and value.
  • Distinguish an estimate from an observed return.

MGT_04

How to avoid catastrophe

Objective

Identify failure scenarios, approval boundaries, stop conditions, and escalation.

Topics covered

  • Failure scenarios
  • Approval boundaries
  • Stopping and escalation

Key learning outcomes

  • Describe a failure scenario and its consequences.
  • Define an approval point and a stop condition.
  • Specify when to escalate a problem.
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AI opportunities

4 modules

OPS_01

Human resources

Objective

Identify HR uses while preserving confidentiality and human judgment.

Topics covered

  • AI uses in HR
  • Confidentiality
  • Human judgment

Key learning outcomes

  • Identify an HR task suited to assistance.
  • Bound the information to share and the decisions to keep human.

OPS_02

Accounting

Objective

Explore document preparation and reconciliation with traceability and review.

Topics covered

  • Document preparation
  • Reconciliation
  • Traceability and review

Key learning outcomes

  • Prepare reconciliation items for checking.
  • Link information to its source document.
  • Identify items needing review.

OPS_03

Project management

Objective

Support planning, status synthesis, dependencies, and follow-up.

Topics covered

  • Planning
  • Progress and dependencies
  • Project follow-up

Key learning outcomes

  • Structure a progress summary.
  • Identify dependencies to clarify.
  • Prepare follow-ups from the plan.

OPS_04

Administration

Objective

Identify request, document, and coordination tasks suited to assistance.

Topics covered

  • Request handling
  • Document preparation
  • Coordination

Key learning outcomes

  • Choose an administrative task to assist.
  • Structure the information in a request.
  • Prepare the material needed for follow-up.
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Designing your internal AI policy

3 modules

POL_01

Balancing risks and opportunities

Objective

Define permitted uses, responsibilities, and proportionate review rules.

Topics covered

  • Permitted uses
  • Responsibilities
  • Review rules

Key learning outcomes

  • Draft a use rule proportionate to risk.
  • Assign review responsibility in the policy.

POL_02

Preserving professional confidentiality

Objective

Identify professional confidentiality obligations and review tool and data boundaries; the course does not replace legal advice.

Topics covered

  • Confidentiality obligations
  • Tool boundaries
  • Data-sharing boundaries

Key learning outcomes

  • Identify information subject to professional confidentiality.
  • Formulate questions to resolve before using a tool.

POL_03

Data security and information leakage

Objective

Address data flows, access, retention, and incident escalation in policy.

Topics covered

  • Data flows
  • Access and retention
  • Incident escalation

Key learning outcomes

  • Map a data flow for the policy.
  • Specify access and retention rules.
  • Describe an incident escalation path.
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Agentic programming

3 modules

DEV_01

Organizing work and avoiding rework

Objective

Structure tasks, version control, tests, and review checkpoints.

Topics covered

  • Task breakdown
  • Version control
  • Tests and review checkpoints

Key learning outcomes

  • Break work into verifiable steps.
  • Plan tests and reviews before integrating changes.
  • Link a change to a version.

DEV_02

Isolation, sandboxing, permissions, and secrets management

Objective

Scope execution environments, least privilege, and secret access within one module.

Topics covered

  • Isolation and sandboxing
  • Least privilege
  • Secret access

Key learning outcomes

  • Bound a task’s execution environment.
  • Restrict permissions to the task’s needs.
  • Define which secrets an agent may use.

DEV_03

When to read the code

Objective

Choose risk-based code review points and validate agent changes.

Topics covered

  • Change risks
  • Review points
  • Change validation

Key learning outcomes

  • Identify changes that need careful reading.
  • Justify a review point based on risk.
  • Check a change proposed by an agent.
Discuss this course →

Cybersecurity in the age of agents

3 modules

SEC_01

Confidential data

Objective

Classify sensitive data and bound agent access and handling.

Topics covered

  • Data classification
  • Agent access
  • Sensitive-data handling

Key learning outcomes

  • Classify a scenario’s data by sensitivity.
  • Define an agent’s permitted access and handling.

SEC_02

Data leaks through prompt injection

Objective

Understand untrusted instructions, exfiltration scenarios, and layered defenses.

Topics covered

  • Untrusted instructions
  • Exfiltration scenarios
  • Layered defenses

Key learning outcomes

  • Spot an untrusted instruction in incoming content.
  • Describe a data-leak scenario.
  • Propose multiple layers of defense.

SEC_03

Proper agent isolation

Objective

Choose execution, network, and tool boundaries, then review residual risks.

Topics covered

  • Execution boundaries
  • Network and tool access
  • Residual risks

Key learning outcomes

  • Choose the access needed for an agent scenario.
  • Describe the selected isolation boundaries.
  • Identify risks that still need review.
Discuss this course →