AI May Recommend. Policy Must Commit
Traditional application security asks: who can access a resource? Agentic AI adds a harder question: what is the system allowed to do when it receives uncertain, incomplete, or maliciously influenced information?
An agent can be authenticated and authorized yet still unsafe. It may follow an instruction hidden in retrieved content, cite an outdated policy, use an overly broad tool, or confidently act beyond its intended authority. These are behavioral security failures—not merely endpoint failures.
This session introduces the LLM-SHIELD lens: Scope, Harden, Isolate, Empower, Live-Defend, and Demonstrate. It provides an architecture-first approach to securing AI capabilities across prompts, retrieval, context, tools, runtime policy, human oversight, and evidence.
Participants will walk through an agentic support workflow facing prompt injection, stale policy retrieval, excessive tool authority, and conflicting customer data. The goal is not to make AI timid. It is to make its actions admissible, contained, and provable.
Attendees will learn:
- Why traditional access control is necessary but insufficient for agents.
- How to prevent retrieval and tool-use failures from becoming business actions.
- How to enforce least authority for agentic workflows.
- How to produce evidence that survives an audit or customer dispute.
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of AI & Data Architecture at Salesforce, where he focuses on enterprise AI, agentic systems, cloud-native architecture, distributed systems, data platforms, security, and large-scale transformation.
Over his career, Rohit has designed and led complex enterprise platforms across AWS, Google Cloud, microservices, real-time data, API ecosystems, resilient distributed systems, and AI-enabled architectures. His work increasingly focuses on the challenges enterprises face as software evolves from deterministic services to AI-native and agentic systems—particularly around reliability, governance, evidence, security, observability, cost, and safe autonomy.
Rohit is the author of System Design with AI Interview Guide: Designing Scalable, Agentic, and Defensible Systems, published by Apress. The book presents a modern approach to system design covering scalability, distributed systems, AI architecture primitives, security, reliability, economics, agentic systems, and real-world architectures including e-commerce, ride sharing, payments, fraud detection, messaging, video streaming, file storage, and search. (Springer Link)
Book:
Amazon: https://a.co/d/09Zs1twa
Publisher / Springer Nature: https://link.springer.com/book/10.1007/979-8-8688-2782-2
O'Reilly: https://learning.oreilly.com/library/view/system-design-with/9798868827822/
Rohit is also an O’Reilly instructor and a frequent speaker at technology conferences including No Fluff Just Stuff, UberConf, GIDS, and other international events. His talks focus on practical architecture lessons from building and operating complex systems, including AI control planes, trusted agents, inference at scale, evidence-first RAG, AI security, distributed-system failure, and AI-era software architecture.
As a trusted advisor and architecture leader, Rohit works at the intersection of business strategy and deep technical architecture—helping teams translate complex business problems into scalable, resilient, secure, and economically sustainable systems.
Rohit holds an MBA in Corporate Entrepreneurship from Babson College and graduate-level education in Computer Science from Boston University and Harvard University.
Connect with Rohit:
LinkedIn: http://linkedin.com/in/rohit-bhardwaj-cloud
X / Twitter: @rbhardwaj1