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- DaysHoursMinSec
As AI rapidly becomes embedded in business processes and decision-making, organisations must adopt a governance, risk, and compliance (GRC) lens to manage both its promise and its exposure. This session explores AI security beyond technical controls, focusing on accountability, policy frameworks, and enterprise risk alignment. We will examine how AI introduces new categories of risk including data privacy concerns, model bias, regulatory uncertainty, and third-party dependencies while amplifying existing cybersecurity and operational risks. Participants will gain insight into emerging regulatory expectations and how to interpret evolving standards such as AI governance frameworks, data protection obligations, and sector-specific compliance requirements. At the same time, AI presents significant opportunities to strengthen organisational resilience. From improving risk analytics and enhancing compliance monitoring to enabling more proactive threat detection, AI can become a strategic enabler when governed effectively. The talk will highlight best practices for integrating AI into existing GRC models, establishing clear accountability, and ensuring ethical and transparent use.
Learning objectives:
By the end of this session, participants will be able to:
1. Describe key AI-related security and compliance risks within a GRC context.
2. Explain how emerging regulations and governance frameworks shape AI risk management practices.
3. Apply GRC principles to assess and manage AI-related risks in organisational settings.
4. Analyse the impact of AI on existing risk, control, and compliance frameworks.
5. Evaluate strategies for balancing AI innovation with governance, trust, and regulatory requirements.
Speaker(s)
Join the launch of the WIPO-ITU IP Management Clinic for AI-driven startups and SMEs. Discover how this four-month program will help you transform your innovation into business value through strategic IP management, expert mentoring, practical tools and tailored guidance. Meet the experts, mentors and partners behind the Clinic, learn what participation entails, and explore the opportunities for collaboration and growth. The session will also feature an interactive exchange on a key IP issue shaping the future of AI entrepreneurship.
Many AI startups build solutions that are technically sound and socially valuable, yet struggle to achieve real-world adoption. This session examines why. Drawing on cross-sector experience in scaling technology through enterprise, government, and public–private ecosystems, the workshop introduces a practical framework for understanding “adoption dynamics” beyond product-market fit. Participants will explore why the end user is often not the decision-maker, how trust and distribution function as hidden infrastructure, and why partnerships frequently determine success more than product features. Rather than focusing on specific industries, the session distills universal patterns that apply equally to healthcare, agriculture, climate systems, and enterprise software. Through case-based discussion and interactive exercises, founders will learn how to reframe their solutions as part of broader systems involving institutions, incentives, and stakeholders. The goal is to help participants move from building products to designing pathways for adoption at scale.
Learning objectives:
By the end of this session, participants will be able to:
1. Understand the difference between product-market fit and real-world adoption dynamics.
2. Analyze the stakeholder and decision-making structures surrounding their solution.
3. Evaluate common scaling barriers, including trust, distribution, and institutional friction.
4. Apply a structured approach to identifying appropriate partners and channels for scale.
5. Create an initial adoption strategy tailored to their specific ecosystem.
Fundraising is not only about technology or impact. It is also about understanding how investors think, assess risk, and make decisions under uncertainty. This masterclass explores the psychology behind venture capital decision-making and how founders can translate complex AI solutions into clear, credible, and compelling narratives without oversimplifying or overselling.
The session focuses on how investors evaluate teams, execution risk, trust, governance awareness, and scalability, particularly in regulated and impact-driven environments such as healthcare, climate, and public-sector AI. Founders will examine common pitch pitfalls, learn how key signals are interpreted during fundraising conversations, and understand how a pitch can be structured to align ambition with realism.
The masterclass is highly practical and interactive, combining real-world examples with frameworks that can be applied immediately in preparation for fundraising, partnerships, and scale within the AI for Good ecosystem.
Learning objectives:
By the end of this session, participants will be able to:
1. Understand how venture capital investors evaluate AI startups beyond technology and traction.
2. Analyze common cognitive biases and decision drivers influencing investor behavior.
3. Evaluate why technically strong pitches often fail to convert.
4. Apply practical frameworks to structure clearer, more persuasive pitch narratives.
5. Refine their own pitch approach to better align impact, scalability, and investor expectations.
Early-stage startups often treat data privacy and cybersecurity as afterthoughts – until it is too late. This masterclass breaks that pattern by showing founders how to build trust, resilience, and compliance into their products from day one without slowing innovation.
We will cover the fundamentals of data protection (what to collect, what to avoid, and how to store it), practical cybersecurity measures that scale with your team, and how to navigate evolving regulations like GDPR. Through real-world startup examples – including breaches, near-misses, and good practices – you will learn what actually works in fast-moving environments.
You will leave with a clear, actionable framework to assess your current risk posture, prioritize security investments, and communicate trust to customers and investors. Whether you are pre-seed or scaling, this session equips you to turn privacy and security into a competitive advantage – not just a compliance burden.
Learning objectives:
By the end of this session, founders will be able to:
1. Analyze their startup’s data lifecycle to identify key privacy and security risks.
2. Evaluate trade-offs between speed, cost, and security when making technical and product decisions.
3. Design a minimum viable security framework tailored to a startup environment.
4. Illustrate how their privacy and cybersecurity practices can enhance customer trust and investor confidence.
