Maish Saidel-Keesing
GenAI Security: Navigating the Unseen Iceberg
#1about 2 minutes
The iceberg metaphor for hidden GenAI risks
GenAI applications have significant underlying complexities and risks that are not visible on the surface, similar to an iceberg.
#2about 3 minutes
Tracing the rapid evolution of GenAI adoption
GenAI has moved from proof-of-concepts in 2023 to production in 2024, leading to emerging risk management challenges.
#3about 2 minutes
Maintaining data integrity for internal and external data
It is crucial to ensure the accuracy of your own data and be aware of potential biases in external data used to train LLMs.
#4about 2 minutes
Managing the non-deterministic nature of large language models
The unpredictable, non-deterministic output of LLMs requires implementing input and output guardrails to ensure reliable and safe responses.
#5about 2 minutes
Evaluating the security risks of third-party AI agents
Using third-party AI agents introduces trust and security risks, as you often cannot inspect the code or verify their behavior.
#6about 4 minutes
Addressing security challenges in RAG and MCP architectures
Implementing RAG or MCP at scale introduces significant security challenges related to authentication, authorization, and overly permissive access.
#7about 3 minutes
Mitigating the enterprise risks of shadow AI usage
Unauthorized use of AI tools by employees, or "shadow AI," creates data leak risks that require clear company policies and education.
#8about 1 minute
Ensuring compliance and auditability for GenAI applications
GenAI systems must be designed with compliance in mind, providing clear audit trails to meet legal and regulatory requirements like GDPR.
#9about 3 minutes
Building resilience against external GenAI service failures
Relying on third-party GenAI APIs creates a dependency that requires a disaster recovery plan to handle outages and prevent cascading failures.
#10about 2 minutes
Predicting future challenges and the need to slow down
The rapid pace of GenAI adoption will likely lead to significant issues like data leaks and outages, forcing organizations to re-evaluate their speed.
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