• IAM systems generate vast amounts of data across hybrid environments, making actionable insights difficult to extract manually.
• GenAI tools like GPT-4 and Claude simplify IAM operations by enabling natural language queries for access, policy and log analysis.
• With rising AI adoption, modern IAM platforms are integrating LLMs and emerging standards like MCP to deliver real-time, intelligent identity management—marking the start of a new, more productive age for IAM users.

Identity and Access Management (IAM) systems are the digital gatekeepers of modern enterprises. IAM protection touches every entity from employees to applications, from web resources to mobile resources. These days, as more and more organizations scale into cloud, SaaS and remote environments, IAM systems are generating more data than ever before. From policies and entitlement rules to authentication events and admin logs, gaining useful insight from growing volumes of IAM-related information is a challenge for even well-resourced teams.

If only that were the only challenge. Increasingly complex and distributed IT environments also have security teams asking:

• Which areas of our environment remain unprotected?

• Are there hidden risks in our access policies?

• Who changed or deleted critical configuration items?

• What are the root causes of recent authentication failures?

We’ve established the problem isn’t a lack of data; there’s plenty of that. The challenge is turning all that data into useful, actionable insights—a task that often requires deep product knowledge and time in the trenches. This is true for virtually every IAM platform, whether it’s SiteMinder, Okta, ForgeRock, Ping or even custom-built systems. 

But no matter what platform you use, generative AI (GenAI) may help you get from insight to action faster.

Why GenAI is a game changer for IAM

As a solution engineer who has worked closely with customers on IAM projects for many years, I’ve seen firsthand how the challenges above can lead to an array of potentially serious issues, including security risks and even service outages. More and more teams are reaching a point where they need faster, smarter ways to understand and manage their IAM environments. 

Increasingly, they’re turning to large language models (LLMs) like GPT-4, Claude and Gemini for help. In the process, their GenAI capabilities are changing how we interact with these systems.

For instance, imagine being able to ask:

• “Is resource path /finance protected by SSO? If so, who can access it?”

• “List inactive admin accounts with high privileges.”

• “Summarize the latest authentication errors and likely fixes.”

Today, answering these questions manually often means digging through multiple tools, scanning logs and cross-referencing JSON/XML outputs or audit trails. GenAI opens a new way to interact with IAM platforms, with the ability to summarize, correlate and explain IAM data through natural language.

The goal isn’t to replace admin skills, but to reduce unnecessary friction. With GenAI, we can make key IAM insights easier to surface, especially for new team members or overworked operations staff.

Why now?

Several trends are converging to make GenAI in IAM not only feasible, but necessary:

• Enterprises are raising the AI bar. In recent conversations with customers, I’ve seen a clear shift: Organizations are moving from exploring to adopting AI. (A recent ISC2 survey bears this out, with 30% of security teams saying they’ve adopted AI for security, and another 42% exploring or testing adoption.) Whether deploying their own LLMs or using cloud-based models, they now expect critical systems like IAM to integrate natively with AI workflows.
• Security and compliance pressures are intensifying. Zero Trust architectures and evolving audit requirements demand faster, more transparent answers to questions around access, privilege and accountability. To meet these expectations, IAM systems increasingly need to deliver contextual insights in real time, pushing teams to extend their existing platforms with intelligence-driven capabilities.
• IAM spans hybrid environments. Access controls now stretch across on-premises, cloud, SaaS, mobile, and mainframe. This complexity demands unified visibility and orchestration, and this is something LLMs can help simplify through correlation and summarization.
• LLMs are rapidly evolving. New language models with larger parameters and stronger reasoning capabilities are being released monthly, and many of those support both cloud and on-premises deployment. This makes it technically feasible for organizations to adopt private or hybrid LLM solutions tailored to their risk and data governance needs.
• Modern IAM is API-first. Most enterprise IAM platforms now expose REST APIs, typically described with OpenAPI specs, for managing policies, configurations, sessions and logs. This creates structured, machine-readable access to the data needed for real-time reasoning and automation.
• LLM-native standards are emerging. Open protocols like MCP (Model Contextual Protocol) are bridging the gap between large language models (LLMs) and operational systems. Techniques such as Retrieval-Augmented Generation (RAG) are also evolving rapidly, enabling LLMs to securely and accurately interface with external knowledge sources like IAM policy stores, logs or entitlement catalogs.

As Vadim Lander, Broadcom’s Identity Security CTO, explains, “Today’s environments demand a modern identity fabric that supports hybrid and multi-cloud infrastructures while adapting to evolving threats.”

With GenAI assistants running atop a well-architected identity fabric, IAM teams gain both clarity and speed; it's essential for securing the digital world at scale.

Supporting daily IAM operations—today and tomorrow

At Broadcom, we see identity and access security as a space where AI can have direct, tangible impact. Our focus isn’t just on integrating AI for its own sake, but on helping administrators answer real-world questions faster and with more confidence. Whether it’s access/policy analysis, log summarization or change tracking, GenAI can support the daily work of IAM operations.

This aligns closely with what we’ve published on cloud transformation in access management and modernizing SSO stacks—both areas where visibility and control become even more critical.

But what about the future? We’re now entering a phase where IAM teams can do more than just use dashboards. They can ask questions or request information directly and get meaningful answers in real time:

• “Show me accounts with dormant privileges created over 90 days ago.”

• “What errors in the last week suggest an authentication misconfiguration?”

• “Highlight policies granting access to contractors or vendors.”

In Part 2 of this two-part series, I’ll share how I’ve started to bring that idea to life with a GenAI assistant prototype built around SiteMinder and using MCP as the bridge. It’s a hands-on look at what’s possible when we combine structured IAM data with intelligent language models—something I think more teams will be experimenting with in the very near future.