Agentic AI shifts identity and access management from static human controls to dynamic, context-driven models that must represent humans, agents, and resources to secure autonomous actions at scale.
Agentic AI performs autonomous actions that traditional, static IAM models cannot safely govern.
Identity context comprises three dimensions: human identity, AI agent identity, and resource identity.
The next wave of AI is not about generating content — it's about taking action in a secure and operationalized manner. For years, organizations focused on static controls of human access to applications, data, and infrastructure. AI agents are performing autonomous tasks on behalf of humans: purchasing goods, processing refunds, accessing databases, interacting with SaaS applications, and making business decisions.
I hope you can join me at Security & Risk Forum this November 9–10 in Washington, DC! At this year's Forrester Security & Risk Forum in November, I'll be delivering a keynote on one of the most significant emerging challenges in AI agent adoption and identity security: how to securely operationalize agentic AI at scale. Static roles and binary allow-or-deny models are no longer sufficient.
Agentic AI is transforming identity and access management. Old identity and access management designs and patterns are inadequate. That approach doesn't work well with agentic AI.
Context engineering is becoming essential as AI agents take on more software development work. An agent can plan, code, test, investigate incidents, trigger CI, and help deploy software. But none of that matters if it is operating without the right information. This is the main problem I keep seeing. We connect an LLM
AI-agent power users curate 3 kinds of context: global (across tasks), local (task-specific), and ambient (raw streams like email).
Enterprise AI has entered a new era. Organizations are rapidly moving beyond assistants that answer questions to autonomous agents capable of reasoning, invoking tools, accessing enterprise applications, coordinating with other agents, and completing multi-step business workflows with minimal human intervention. This s
Enterprises must know whether they can trust the environment in which an agent operates.
First, for some context: AI is creating a resurgence for “context” in the industry lexicon. Large language models rely on context to understand intent, resolve ambiguity, and generate accurate responses. As consumers and organizations adopt new AI technology, context plays a key role in creating relevant customer-brand
Your customers expect consistent, instant, intelligent service across every channel, even when they bounce between SMS, email, and web chat from one interaction to the next. But your team can’t be everywhere at once—no one can. That’s exactly where agentic AI in retail comes in. Agentic AI gives customers a seamless ex
Loading more related stories...
Open the app view to save this story, compare related coverage, and continue from the same source.