{"product_id":"llm-safety-course-conversational-ai-compliance","title":"Conversational AI \u0026 LLM Safety (Compliance Focus)","description":"\u003cp dir=\"ltr\"\u003e\u003cspan\u003eConversational AI systems can improve customer service, internal support, research and workplace productivity, but they also create identifiable compliance risks. Large language models may generate false information, expose confidential data, produce discriminatory outputs, follow malicious instructions or take actions beyond their intended authority. When these systems are used in recruitment, healthcare, finance, education, customer communications or public services, weak controls can lead to consumer harm, privacy breaches, regulatory scrutiny and poor business decisions.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThis \u003c\/span\u003e\u003cspan\u003eLLM safety course\u003c\/span\u003e\u003cspan\u003e explains how organisations can control those risks before and after deployment. Learners will examine practical measures such as AI use-case approval, output verification, risk classification, vendor due diligence, access restrictions, prompt-injection testing, incident escalation, audit trails and continuous monitoring. The course is designed for professionals who need to oversee AI systems responsibly rather than develop machine-learning models.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cstrong\u003eWhat Is an LLM Safety and Conversational AI Compliance Course?\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAn LLM safety and conversational AI compliance course teaches professionals how to assess and control risks created by chatbots, copilots, generative AI tools and AI agents.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe course focuses on the points where organisations commonly lose control: inaccurate outputs, excessive user trust, confidential information entered into public tools, discriminatory recommendations, insecure integrations, weak vendor oversight and unclear accountability. Learners will examine how legal, privacy, security, product, human resources and operational teams should work together when approving and supervising AI use.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe training also introduces recognised approaches to AI risk management, including the NIST AI Risk Management Framework, the NIST Generative AI Profile and ISO\/IEC 42001. These frameworks support structured governance, documented risk assessment, testing, monitoring and continual improvement throughout the AI system lifecycle.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eProfessionals who need a broader foundation in organisational AI oversight may also benefit from the\u003c\/span\u003e\u003ca href=\"https:\/\/spanishcomplianceinstitute.net\/products\/ai-governance-responsible-ai-fundamentals\"\u003e\u003cspan\u003e \u003c\/span\u003e\u003cspan\u003eAI Governance \u0026amp; Responsible AI Fundamentals course\u003c\/span\u003e\u003c\/a\u003e\u003cspan\u003e.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cstrong\u003eWho Needs Conversational AI and LLM Safety Training?\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThis course is suitable for professionals who approve, purchase, configure, supervise or rely on conversational AI systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eCompliance and legal teams\u003c\/span\u003e\u003cspan\u003e responsible for identifying regulatory duties, reviewing AI claims and documenting controls.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003ePrivacy professionals\u003c\/span\u003e\u003cspan\u003e assessing how prompts, outputs, logs and connected data sources may contain personal information.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eCybersecurity teams\u003c\/span\u003e\u003cspan\u003e managing prompt injection, data leakage, insecure integrations, excessive permissions and AI-enabled misuse.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eAI product managers and technology leaders\u003c\/span\u003e\u003cspan\u003e responsible for use-case approval, testing, release decisions and ongoing performance.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eRisk and internal audit teams\u003c\/span\u003e\u003cspan\u003e reviewing AI inventories, control evidence, monitoring records and incident handling.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eHuman resources and recruitment teams\u003c\/span\u003e\u003cspan\u003e evaluating AI-supported screening, assessment and workforce-management tools.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eProcurement and vendor-management teams\u003c\/span\u003e\u003cspan\u003e reviewing provider contracts, model limitations, data use and third-party assurance.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eCustomer-service and operations teams\u003c\/span\u003e\u003cspan\u003e supervising AI-generated responses and escalating unsafe or misleading outputs.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eProfessionals entering AI governance, responsible AI or technology-risk roles\u003c\/span\u003e\u003cspan\u003e who need practical compliance knowledge.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cstrong\u003eWhat Does an LLM Safety Course Cover?\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe course covers the controls required to manage conversational AI from initial approval through ongoing use. Learners will examine how to classify AI use cases, identify affected users, assess data sensitivity, define human-review requirements and determine when an AI-generated response must be blocked, corrected or escalated.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eKey topics include:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eHallucinations, inaccurate responses and unsupported AI claims\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eBias, discrimination, disparate impact and fairness testing\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003ePrompt injection, jailbreaks, data leakage and malicious use\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eHuman oversight, output verification and escalation procedures\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eFTC consumer-protection expectations and deceptive AI claims\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003ePrivacy and confidentiality duties under laws such as COPPA, HIPAA, GLBA and FERPA\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eEmployment AI, civil-rights risks and bias-audit responsibilities\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eAI inventories, risk classification and approval workflows\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eVendor due diligence, contract controls and third-party assurance\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eAcceptable-use policies and employee AI restrictions\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eRed teaming, model evaluation, guardrails and content filtering\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eAccess controls, logging, retention and incident-response evidence\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli dir=\"ltr\" aria-level=\"1\"\u003e\n\u003cp dir=\"ltr\" role=\"presentation\"\u003e\u003cspan\u003eContinuous monitoring and responsible deployment\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThe detailed curriculum explains how these controls can be applied across legal, technical and operational functions.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2 dir=\"ltr\"\u003e\u003cstrong\u003eWhy Can Poorly Controlled LLM Use Create Legal and Business Risk?\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAI outputs may be inaccurate but appear convincing.\u003c\/span\u003e\u003cspan\u003e A chatbot can invent policies, product details, legal information or customer entitlements. Without verification and escalation rules, employees and customers may act on information that was never approved by the organisation.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eSensitive information may be disclosed through prompts, outputs or logs.\u003c\/span\u003e\u003cspan\u003e Staff may enter customer records, health information, financial data, employee details, intellectual property or confidential documents into an AI tool. Risk increases when the organisation has not reviewed how the provider stores, reuses, retains or transfers that information.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eAI-supported decisions may create discriminatory outcomes.\u003c\/span\u003e\u003cspan\u003e Recruitment, employee assessment, credit, healthcare and education tools can produce unequal results if training data, model behaviour or decision criteria disadvantage particular groups. Human review alone is not sufficient when reviewers do not understand the system’s limitations or cannot challenge its output.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003ePrompt injection and insecure AI agents can bypass intended controls.\u003c\/span\u003e\u003cspan\u003e Malicious instructions may cause an LLM to reveal protected information, ignore system rules, misuse connected tools or perform unauthorised actions. These risks require testing, permission controls, secure integration design and clear limits on what the system can access.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eWeak governance makes incidents harder to investigate.\u003c\/span\u003e\u003cspan\u003e Organisations need records showing who approved the use case, what testing was completed, which data sources were connected, what safeguards were applied and how incidents were corrected. Without this evidence, accountability is unclear and repeated failures become more likely.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan\u003eThis course helps learners move from general AI awareness to practical oversight. By the end of the training, learners will be better prepared to question AI outputs, identify control gaps, support compliance reviews and contribute to safer deployment decisions.\u003c\/span\u003e\u003c\/p\u003e","brand":"Spanish Compliance Institute","offers":[{"title":"Default Title","offer_id":53602322907483,"sku":null,"price":44.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0963\/1118\/1659\/files\/conversational-ai-llm-safety-compliance-course.webp?v=1784181163","url":"https:\/\/spanishcomplianceinstitute.com\/products\/llm-safety-course-conversational-ai-compliance","provider":"Spanish Compliance Institute","version":"1.0","type":"link"}