Imagine a world where security risks are identified and mitigated before they even happen. Imagine if access and auditing recommendations were made with the precision of a computer algorithm. Imagine the power of artificial intelligence to revolutionize your organization's identity governance and administration practices. We have reached an inflection point in the world of AI. The release of technologies like ChatGPT is as monumental as the development of the internet or Google as a search engine. AI is going to change the way we think about IGA. With the capabilities of AI, IGA solutions can provide a higher level of granularity, faster data processing, and more complex analysis to better identify and mitigate risks in real-time. This will lead to more effective risk management, smarter access and auditing recommendations, and increased automation of processes and operations. But this is just the beginning. As this technology continues to evolve, we can expect to see even greater benefits from AI-enabled IGA solutions in the future. Imagine a future where your organization's security is not just protected but anticipates and proactively prevents threats before they happen. So I ask you, are you ready to join us in shaping the future of IGA? Are you ready to harness the power of AI to revolutionize your organization's identity governance and administration practices? The future is here, and it's time to embrace it.
In this talk, attendees will learn about the potential for AI to revolutionize Identity Governance and Administration (IGA) practices.
This presentation will provide an overview of the terminology and basics of AI and ML in the context of Identity and Access Management (IAM) and Identity Governance and Administration (IGA). It will explore a number of current use cases for leveraging ML in IAM, demonstrating the benefits of automation and enhanced security that ML can bring to identity management. The presentation will conclude with strategic considerations for using ML in IAM, highlighting the importance of considering business value, available data, and existing technologies when implementing ML-based solutions for identity management.