Adopt generative AI productively and responsibly. This track pairs hands-on generative-AI adoption across real workflows with ethical-AI governance — bias mitigation, data privacy and compliant deployment.
Generative AI Is Outrunning Your Governance
Employees are already pasting sensitive data into generative tools and shipping AI-assisted work faster than any policy can keep up. Left ungoverned, that creates real exposure — bias, privacy leakage, lost IP and hallucinated output presented as fact. Emerging regulation such as the EU AI Act and frameworks like the NIST AI RMF are turning responsible AI from a nice-to-have into an obligation.
A practical blend of generative-AI adoption and responsible-AI governance your teams can apply immediately — every highlight below maps to a module in the detailed course syllabus.
Practical generative-AI adoption across real, day-to-day workflows
Effective prompting patterns that make output reliable and reusable
Bias, fairness & data privacy across inputs, models and outputs
Responsible & compliant deployment with human oversight built in
AI governance & the EU AI Act — risk tiers, obligations and the NIST AI RMF
Three delivery depths — from a leadership briefing to a full hands-on lab — all tailored to your workflows and team.
A leadership session that builds shared urgency and shapes your AI policy and governance stance — no technical prerequisites.
For teams adopting generative AI: applied workflows, prompting patterns and the governance to deploy responsibly, with guided demos.
Full immersion: build vetted gen-AI workflows, a prompt playbook and a responsible-AI policy, and ship a capstone governance plan.
No technical prerequisites — the track is open to any team adopting generative AI
Basic comfort with common AI tools helps, but is not required to take part
An interest in using generative AI responsibly within your own workflows
Hands-on workflow exercises using everyday business scenarios — no local setup needed
Prompt libraries and quick-reference cards to keep and adapt to your work
A responsible-AI policy & governance template you can put to work immediately
Content is pitched to each audience so workers, leaders and risk owners all leave with what they need.
Use generative AI day-to-day for content, analysis and knowledge work.
Leave able to apply gen-AI productively and safely with reliable prompting habits.
Decide where AI is embedded into products, services and operations.
Leave able to prioritise high-value use cases and set guardrails for adoption.
Set policy, manage exposure and answer to regulators and the board.
Leave with a governance baseline mapped to the EU AI Act and the NIST AI RMF.
Capabilities and tangible artifacts that translate directly into safe, productive AI adoption.
A shared, accurate mental model of where generative AI helps and where it risks harm
Hands-on skill applying gen-AI to real workflows with reliable prompting patterns
Practical techniques to detect bias, protect privacy and verify AI output
Confidence to deploy generative AI in a trustworthy, compliant way
A set of vetted generative-AI workflows ready to use across your team
A prompt & usage playbook capturing patterns that work for your context
A responsible-AI policy template you can adopt and adapt internally
An EU AI Act / NIST AI RMF readiness checklist to assess your posture
A QSECS certificate of completion for every participant
Five modules scaling from applied adoption to AI governance and the EU AI Act. Select a module to expand it.
How large language and multimodal models work — only the intuition you need to use them well.
Spotting where gen-AI genuinely helps across content, analysis and knowledge work.
Embedding AI into day-to-day tasks without disrupting how your team already works.
Hallucination, brittleness and overconfidence — and how to recognise them early.
Structure, context and instructions that make output consistent and useful.
Building a prompt library your team can share, adapt and trust.
Chaining steps and using AI as a drafting, review and synthesis partner.
Fact-checking, citation discipline and keeping a human in the loop on decisions.
How bias enters through data, models and the way prompts are framed.
Practical checks and mitigations across inputs, outputs and evaluation.
What is safe to share, protecting IP and avoiding sensitive-data leakage.
Designing AI-assisted processes that treat people and cases equitably.
Deciding where people must stay in or on the loop for AI-assisted work.
Labelling AI involvement and being clear with users and stakeholders.
Prompt injection, data exfiltration and guarding against unsafe use.
Acceptable-use rules, review gates and monitoring for live AI workflows.
Why governance matters and how the major frameworks fit together.
The four risk tiers — unacceptable, high, limited and minimal — and what each obliges.
The four core functions — Govern, Map, Measure and Manage — applied to your AI use.
Draft and present a responsible-AI policy and readiness checklist for a realistic scenario.
Awarded by QSECS · Quantum Security Solutions
Every participant who completes the track receives a verifiable QSECS Certificate of Completion — a credible signal to leadership, auditors and customers that your teams can adopt generative AI productively and responsibly.
Individually issued with a unique, verifiable credential ID
Hands-on and lab tracks include a graded capstone assessment
Maps to continuing-education (CPE) hours for common security certifications
Shareable to LinkedIn and your internal skills matrix
An illustrative Day 1 from the 2-day format — every agenda is tailored to your goals before delivery.
Framing where gen-AI helps, where it risks harm and what "responsible adoption" means for your organisation.
Hands-on use across real tasks, with reusable prompt patterns and output verification.
Detecting and reducing bias, protecting confidential data and keeping a human in the loop.
Guardrails, oversight and disclosure — and how they land in real AI workflows.
Build a vetted gen-AI workflow with prompts, checks and a usage policy attached.
Day 2 covers AI governance, the EU AI Act and NIST AI RMF, and a responsible-AI policy & readiness-checklist workshop.
Everything teams usually ask before booking the applied & ethical AI track.
Anyone adopting generative AI — knowledge workers, product and operations leaders, and risk, legal and compliance teams. There are no technical prerequisites; basic comfort with common AI tools helps but is not required, and we pitch each session to its audience.
The track is deliberately tool-agnostic. We teach durable skills — prompting patterns, verification, bias and privacy practice and governance — that transfer across whichever generative-AI tools your organisation uses, and we can work examples in the platforms your team already has.
All three. We run sessions in-person at your site, fully remote, or hybrid — across time zones for distributed teams. The hands-on exercises run in a browser so delivery mode never changes the experience.
A full module covers bias, fairness and data privacy, and another covers governance against the EU AI Act and the NIST AI RMF. Teams leave with a responsible-AI policy template and a readiness checklist they can use to assess and document their own posture.
Yes. We tailor workflows, examples and policy scenarios to your sector and its regulatory context, and can anchor the governance workshop to your real use cases under NDA. Tailoring is scoped during the requirement-analysis call.
Yes — every participant receives a verifiable QSECS Certificate of Completion, and hands-on tracks include a graded capstone. The credential maps to CPE hours for common security certifications.