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AI, Automation and the Law: Strategies for Compliance and Governance

Overview 

AI and automation are advancing faster than the laws intended to regulate them. This has left businesses, legal practitioners and policymakers operating within uncertain frameworks while already facing risks such as ownership disputes over AI-generated content and compliance challenges under privacy laws like POPIA. 

This webinar explores the regulatory gap and shows how those using, advising on or overseeing AI can proactively anticipate risks by establishing governance structures for oversight, drafting contracts that allocate accountability and addressing ethical considerations before disputes or regulatory action arise. 

Join Adv Dwight Snyman as he equips legal practitioners and professionals with a structured framework to understand, manage and mitigate the legal risks of AI and automation. 

Learning objectives 

Attending this webinar will equip you with the following skills: 

• A grounded awareness of the current legal landscape; understanding both South African and international contexts to recognise where the rules are firm and where they are still developing. 

• The ability to identify key areas of legal exposure including, intellectual property, data protection, liability, contractual risk and employment issues. 

• Practical strategies for managing AI-related risks such as drafting stronger contractual clauses and establishing governance processes that ensure oversight and accountability. 

• Greater confidence in providing legal or strategic advice by using established legal principles and proactive policy-making to navigate areas where regulations remain uncertain. 

• Developing a proactive mindset toward shaping AI and automation practices by seeing yourself as an active participant in creating safe, ethical and legally sound systems rather than waiting for regulations to dictate action. 

Content 

The webinar will cover the following topics: 

• Introduction to AI and Automation in Legal Context 

  • Definition and scope of Artificial Intelligence (AI) and Machine Learning (ML) 

  • Automation technologies and autonomous systems 

  • Generative AI and Large Language Models (LLMs) 

  • Distinction between narrow AI, general AI, and artificial superintelligence 

  • Regulatory lag and emerging legal frameworks  

• Intellectual Property (IP) Regime Implications 

  • Authorship and ownership of AI-generated works 

  • Copyright subsistence and originality requirements 

  • Determining the “author” in AI-assisted creation 

  • Patent law and AI-generated inventions 

  • Inventorship requirements under patent statutes 

  • Patentability criteria and inventive step analysis 

  • Trade secrets and confidential information 

  • AI system training on proprietary datasets 

  • Misappropriation risks in algorithmic outputs 

  • Licensing and contractual IP provisions 

  • Allocation of rights in AI service agreements 

  • Moral rights and waiver clauses 

• Data Protection and Privacy Law 

  • Regulatory compliance frameworks 

  • Protection of Personal Information Act (POPIA) 

  • General Data Protection Regulation (GDPR) comparative obligations 

  • Automated decision-making provisions 

  • Right to human intervention 

  • Transparency, explainability, and algorithmic accountability 

  • Lawful processing principles 

  • Consent, legitimate interests, and minimality 

  • Cross-border data transfer restrictions 

  • Data anonymisation and pseudonymisation 

  • Techniques and legal sufficiency tests  

• Liability Regimes 

  • Delictual liability (tort law) in AI context 

  • Negligence and foreseeability in autonomous decision-making 

  • Duty of care in AI system deployment 

  • Product liability and defective software 

  • Applicability of strict liability principles 

  • Distinction between hardware and software defects 

  • Vicarious liability 

  • Liability allocation between developers, deployers, and users 

  • Contractual liability 

  • Breach of AI performance warranties 

  • Indemnity and limitation of liability clauses 

• Contractual Governance 

  • AI-specific clauses 

  • Model performance standards and service-level agreements (SLAs) 

  • Data use restrictions and audit rights 

  • Risk allocation provisions 

  • Liability caps for algorithmic errors 

  • IP indemnities and infringement warranties 

  • Termination triggers 

  • Regulatory non-compliance 

  • Material change in AI system capabilities 

• Employment and Labour Law Implications 

  • Automation-induced workforce reductions 

  • Retrenchment procedures under labour statutes 

  • Algorithmic management and surveillance 

  • Monitoring compliance with workplace privacy rights 

  • Discrimination risks in AI recruitment tools 

  • Indirect bias and disparate impact analysis 

• Consumer Protection 

  • Disclosure requirements 

  • Duty to inform consumers of AI interaction 

  • Misrepresentation and deceptive practices 

  • AI-generated content in advertising and commerce 

  • Consumer redress mechanisms 

  • Complaint handling for automated decision-making 

• Regulatory and Policy Developments 

  • International instruments 

  • EU AI Act risk classification system 

  • OECD and UNESCO AI ethics guidelines 

  • South African policy proposals 

  • SABS technical standards for AI systems 

  • Potential AI-specific legislative reforms 

• Ethical and Governance Frameworks 

  • Algorithmic accountability 

  • Model documentation and audit trails 

  • Bias and fairness assessments 

  • Statistical parity and disparate treatment analysis 

  • Human-in-the-loop oversight 

  • Hybrid decision-making structures 

• Risk Management and Compliance Strategies 

  • AI risk registers and governance committees 

  • Periodic model validation and compliance audits 

  • Incident reporting protocols for AI failures 

  • Continuous legal horizon scanning for AI developments