AI Safety & Data Security For All Employees in 2026

Air Canada's chatbot gave incorrect bereavement refund policy information, leading to a court ruling that the company is responsible for ai outputs; implement checks, human-in-the-loop, and restricted chatbot authority.

Understand a three-tier ai security framework: public, enterprise, and on-premise models. Learn how encryption, zero data retention, and retrieval augmented generation protect company information.

Public LLMs like ChatGPT, Gemini, Copilot, and Claude offer helpful capabilities for broad, low-risk tasks. Avoid entering sensitive data, as inputs may be stored or reviewed.

Illustrate tier three on-premise large language models, keeping data within company environments, ensuring privacy, isolation, and strict regulatory compliance. Highlight data residency, access control, and isolation for high-stakes workflows.

Assess how to apply the tier framework to real tasks, from drafting a post to debugging proprietary code, while considering shadow AI, confidential data, and cloud or on premise safeguards.

Examine how Replika's data privacy breach led to a 5.6 million euro fine, highlighting inadequate age verification, unlawful processing of minors' data, and GDPR non-compliance.

Identify government ids, employee numbers, biometric data, and sensitive data such as healthcare and finance, and explain why they must not be put into public tiers for privacy and security.

Apply data privacy basics to protect confidential company information and PII, anonymize content before sending to AI, and select appropriate retention tiers.

Master safe, effective prompts using chain of thought prompting, demand evidence and official sources, and ask for uncertainty to reduce hallucinations and ensure verified outputs.

Demonstrates using official sources and knowledge bases with AI, referencing UK Civil Aviation Authority regulations PDF and linking with page numbers for facts and instructions.

Explore how large language models hallucinate and practice detecting false outputs by cross-checking results from Copilot, ChatGPT, Grok, Gemini, Claude, and DeepSeek in a hallucination hunt.

Master prompt engineering to achieve accurate outputs by applying a clear prompt structure: task, role, limits, guidance, context, steps, templates, and examples.

Discover special prompt engineering tips to keep prompts safe and accurate, such as challenging assumptions, exploring multiple perspectives, and giving AI space to think for better outputs.

Compare ChatGPT and Gemini to reveal discrepancies in top speeds for production cars. Highlight how prompt precision and authentic sources improve accuracy and promote AI safety.

Explore how Amazon's AI recruiting model learned from biased historical resumes, amplifying gender discrimination; learn bias detection, testing methods, and the importance of human in the loop and supervised learning.

Ensure human responsibility for AI results by reviewing and validating outputs. Pause to assess fairness, safety, privacy, hallucinations, memory, and appropriateness, escalating when uncertain or risky.

Explore human in the loop and its oversight levels, including a human reviewer, to ensure artificial intelligence outputs meet standards with escalation when needed.

Explore frameworks for ethical and responsible AI use across personal, organizational, and professional levels, emphasizing transparency, data privacy, compliance with laws, and ongoing AI ethics awareness.

Identify ownership of AI-generated content in the workplace, assess licensing and copyright risks, and avoid plagiarism by verifying sources and crediting authors.

Explore key regulatory frameworks and industry limits on ai use, including the eu ai act, the nist ai framework, and gdpr, with practical guidance for compliant, responsible applications across sectors.

Explore the EU AI Act and the Brussels effect, showing how 2024 EU standards set the baseline for global AI regulation and simplify compliance for major providers.

Organizations must conduct risk management for high-risk AI, document risk register and data used, ensure meaningful human oversight with override rights, and test for accuracy, reliability, and bias.

reduce the amount of data you store and process to minimize risk. ensure data remains accurate, secure, and up to date while complying with GDPR and privacy laws.

Verify AI outputs for accuracy, bias, and compliance by cross-checking facts with trusted sources, ensuring transparent reasoning, and applying deployment due diligence with human review.

Follow your company AI policies and use only approved tools to protect personal and confidential data. Assess whether data is personally identifiable or sensitive and request human review when needed.

Research your organization's AI policies, governance groups, data security rules, and data controller roles by consulting intranet resources and your manager to know what to follow and stay compliant.

Select the appropriate AI tier for each task, protect personal and confidential data, craft clear prompts, verify outputs, and stay aware of bias, fairness, and data policies.

Apply practical takeaways for safe AI use and data security at work, and plan the next steps after this course. Explore bonus courses and bespoke training from Learn Management online.