Can CIAM and AI Thrive Together (CIAM Part 1)
This is the second topic in the Practitioner's Guide to AI Series
TL;DR
One of my particular passions, as a CTO/CPO with years of experience in platform (and SaaS) development, is identity management. As I've been advising companies in this new AI-driven economy as a practitioner, I smile every time I see the CIAM acronym as I explain the power in those words. Embedded in the term is a cool play on lettering -- Customer Identity and Access Management has literally contained "AI" in the middle all along. What seemed like a coincidence now feels like destiny. The identity management systems that took nearly a decade to adapt to cloud and mobile are now facing their true purpose. AI agents, autonomous systems, and generative AI assistants are creating what I call "non-human identities" that need authentication, authorization, and governance at machine speed. While established players like Okta and Ping Identity work to evolve their platforms, the most interesting developments are happening around behavioral analytics, conversational authentication, and dynamic identity orchestration. The organizations that solve this challenge first won't just secure AI -- they'll make customer identity truly universal.
The Identity Challenge Hidden in Plain Sight
If you're not familiar with CIAM, here's a quick summary. CIAM is the framework that handles authentication, authorization, user management, data security, single sign-on, and multi-factor authentication for users accessing work systems, smart apps, or online websites. In other words -- who you are, what you can see, what you can do, and when you can do it.
Right now, I'm very excited. For twenty years, CIAM platforms have operated on a core assumption: there's a human behind every authentication request. We've managed service accounts and API keys, certainly, but those were exceptions in a human-centric system. The "AI" in CIAM was just letters -- until now.
What's remarkable is how the industry seems unprepared for what's already happening -- even though the clue was in the name all along. Research from Deloitte suggests that by 2027, half of all companies using generative AI will deploy AI agents in production. Some organizations are already running hundreds of AI sales representatives and thousands of customer service agents. These aren't simple chatbots -- they're autonomous entities making decisions, accessing sensitive data, and interacting with customers in ways that blur traditional boundaries.
The reality is that these AI agents also need identities. They must authenticate to systems, receive authorization for specific actions, and have their activities monitored and governed. Here's where it gets complex: unlike humans, AI agents can spawn other agents, dynamically delegate tasks, and operate at speeds that make traditional authentication workflows seem glacial.
Perhaps CIAM was always meant to evolve this way. Customer Identity was never just about humans -- it was about any entity that interacts with your business systems. The "AI" hiding in the acronym is finally coming to life.
Current State: Fulfilling CIAM's Original Promise
In my experience implementing enterprise identity systems, I've observed how slowly these platforms typically evolve. The migration from on-premises to cloud took most of a decade. Mobile-first authentication is still being refined. But now we're not just asking these systems to handle a new paradigm; we, as practitioners, are going to be critical in helping them operate as a unified, intelligent fabric.
Here's how I see the major players approaching this transformation, based on their current moves and strategic positioning:
Okta's Dual-Track Strategy
Okta is pursuing what I call a "hedged bet" approach -- building AI capabilities on both sides of their platform while keeping options open. Their Identity Threat Protection with Okta AI focuses on the workforce side, using behavioral analytics to detect threats during active sessions and enabling universal logout across applications. Meanwhile, their Auth for GenAI product tackles the customer identity side, specifically targeting developers building GenAI applications.
What's telling is how they've structured this: two separate products rather than one unified platform. This suggests they're still figuring out whether AI will transform existing CIAM workflows or create entirely new categories. The smart money suggests they're positioning to own both scenarios -- if AI agents become pervasive, Auth for GenAI captures that market; if traditional CIAM gets smarter, Identity Threat Protection leads that evolution.
The gap I see in their current approach is the lack of integration between these products. True AI identity management will likely require seamless coordination between workforce and customer identity -- something their current architecture doesn't fully address.
Ping Identity's Infrastructure Play
Ping has been quieter about AI-specific features, which could indicate either strategic caution or a different approach entirely. Given their strength in complex enterprise deployments and federated identity, I suspect they're building AI capabilities deeper in the stack -- focusing on the protocols and infrastructure that will need to handle machine-speed authentication rather than flashy user-facing features.
This could be the smarter long-term play. While competitors build AI-specific products, Ping may be ensuring their core identity fabric can handle whatever AI throws at it. The risk is getting leapfrogged by more visible innovation; the upside is having the most robust foundation when the AI identity demands really hit enterprise scale.
The Technical Challenges Driving Innovation
Traditional CIAM systems face three fundamental problems when handling AI agents, and the industry's response is creating entirely new approaches to identity management.
Licensing and Rate Limiting at Machine Pace
When I deploy traditional CIAM for human users, I plan for predictable authentication patterns during business hours. AI agents don't follow human patterns. A single marketing AI might spawn hundreds of content generation agents simultaneously, each requiring authentication to different creative tools, brand databases, and approval workflows.
The real challenge isn't infrastructure capacity; individual logins are lightweight, even at scale. The problems are licensing models based on Monthly Active Users (MAUs) and rate limiting, designed to prevent abuse. Most CIAM platforms charge per authentication event or active user. When an AI agent spawns 100 sub-agents that each authenticate multiple times per hour, licensing costs can explode overnight. Worse, legitimate AI authentication patterns can trigger abuse protection mechanisms designed for human behavior, causing the system to block valid requests. CIAM and Security Information and Event Management (SIEM) systems will need to be able to distinguish between human and AI users to apply appropriate security and pricing policies.
Identity Delegation and Chain-of-Custody
The innovation response is addressing how AI agents create and manage other agents. When humans delegate authority to other humans, it's straightforward. AI agents can spawn sub-agents, each with distinct authentication needs. Consider a customer service AI that creates specialized agents for billing inquiries, technical support, and order processing. Each requires appropriate access, but the delegation chain, as well as the chain of custody, can become complex quickly. Many security systems require a historical record of CRUD (create, read, update, delete) for all objects or files.
The audit trail becomes equally complex. When a contract gets modified, you need to trace not just which agent made the change, but the entire delegation chain: which human authorized the sales agent, which business rule triggered the contract agent's creation, and what data influenced each decision point.
Cross-System Identity Propagation
AI agents working on customer issues may access CRM systems, payment processors, shipping providers, and support ticketing systems, each potentially managed by different identity providers. While the authentication itself is fast, managing thousands of concurrent sessions with different permission sets, expiration times, and refresh cycles creates challenges in managing authentication tokens (the digital credentials that prove identity) and downstream authorization calls that traditional federation (where multiple systems share identity information through trusted protocols) wasn't designed to handle at machine speed.
We need new approaches to identity propagation that can keep up with AI agents. This might involve lightweight authentication tokens or cryptographic signatures that systems can validate locally, without having to check back with a central identity server for every request.
Competitive Landscape: Positioning and Opportunities
Looking at market dynamics, traditional CIAM vendors are racing to adapt. Here's my assessment:
The Incumbents' Advantages:
Okta, Ping Identity, Google, and Microsoft have established enterprise relationships and technical depth
They understand compliance and governance requirements that startups often overlook
Their existing customer base provides real-world data for AI model training
The Disruptors' Opportunities:
Startups like Pangea and Descope are building AI-first identity platforms without legacy constraints
They can move faster and take bigger risks with novel approaches
They're not burdened by backward compatibility requirements
The Dark Horses:
Don't underestimate the hyperscalers. AWS, Google Cloud, and Azure have AI expertise and infrastructure to build compelling identity solutions
Companies like Transmit Security are experimenting with AI-powered threat detection and response
What This Means for Your Organization
If you're a technology leader wrestling with these challenges, here's how to think about it:
Now
Audit Your Non-Human Identities: Create a comprehensive inventory of service accounts, API keys, and automated systems. Most organizations have more than they realize.
Audit your licensing headroom: Review the pricing model and usage limits of your CIAM and CIEM platforms. Calculate what happens if AI agents increase your authentication volume by 10x or 100x. Understand whether you pay per user, per authentication event, or per session—this will directly impact your AI strategy costs.
Assess Your Current CIAM Platform: Evaluate whether it can handle machine-speed authentication and support fine-grained, dynamic authorization. If not, begin planning your evolution.
Develop AI Authentication Policies: Start defining how your organization will authenticate and authorize AI agents. Don't wait for vendors to solve this completely.
Next
Pilot Behavioral Analytics: Begin collecting baseline data on how your AI systems interact with your infrastructure. You'll need this foundation for future anomaly detection.
Implement Dynamic Authorization: Create a layer that can adjust authentication requirements based on context, even if it starts simply.
Build AI Governance Framework: Establish policies, procedures, and accountability structures for AI identity management -- this extends beyond technology.
Later
Prepare for Identity Type Differentiation: CIAM platforms will likely adapt or add flags to distinguish between human and AI agent identities, enabling different pricing models and security policies for each. Audit your current non-human identities and understand how your vendor plans to handle the economics of AI agent authentication.
Research and Invest in Identity Intelligence: The winning platforms will make intelligent, context-aware decisions about authentication and authorization in real-time.
Prepare for Compliance Requirements: AI identity management will become an integral part of existing compliance frameworks, including PCI DSS, SOC 2, industry-specific certifications, and content licensing agreements. Media companies, financial institutions, and healthcare organizations will require detailed audit trails for the actions of AI agents. Position yourself ahead of these requirements.
The Reality of Where This Is Heading
Here's what concerns me most: we're building authentication systems for a world where the majority of "users" may not be human. This fundamentally challenges established security philosophies:
Zero Trust Gets More Complex: The "never trust, always verify" principle becomes exponentially more difficult when verification subjects are algorithms that can modify themselves, spawn other agents, or operate across multiple contexts simultaneously. Traditional zero trust assumes you can establish and maintain a baseline of normal behavior -- AI agents may not have consistent behavioral patterns.
Least Privilege Requires New Definitions: Granting minimum necessary access becomes complicated when an AI agent's "necessary" permissions can change dynamically based on its current task, learning state, or the data it encounters. An AI agent processing customer support tickets might need different access levels for billing issues versus technical problems, all within the same session.
Defense in Depth Needs New Layers: Traditional layered security assumes human decision-makers at critical points. When AI agents operate autonomously across multiple security boundaries, we need algorithmic checkpoints that can make nuanced decisions about risk and authorization without human intervention.
Trust Becomes Code: Instead of trusting people, we'll trust lines of code to verify other lines of code. This creates circular dependencies where the security of your identity system depends on the integrity of the agentic systems it's designed to control.
Identity Becomes Contextual: An AI agent's identity might change based on its current task, training state, or risk profile. Static role-based access control breaks down when roles are fluid and context-dependent.
Authentication Becomes Continuous: The concept of "logging in" becomes obsolete when every action requires real-time verification based on current context, risk assessment, and delegation chains.
The organizations that understand this shift and build for it will have a significant advantage. Those that don't will find themselves managing technical debt that makes previous system migrations look straightforward by comparison.
Your Next Steps
This transformation is happening whether we're prepared or not. AI agents are already in production environments, making decisions that affect customers and business operations. The question isn't whether you need to adapt your identity management strategy -- it's how quickly you can do it while maintaining operational stability.
Start with a fundamental question: If an AI agent needed to perform a critical business function in your organization today, how would you authenticate it? How would you authorize its actions? How would you audit its decisions?
If you don't have solid answers to those questions, it's time to start developing them. In the near future, those AI agents won't be theoretical -- they'll be requesting access to your systems, credentials ready, prepared to work.
The organizations that solve this challenge first won't just have better security. They'll have made Customer Identity truly universal -- finally fulfilling what those two letters in CIAM always promised. The opportunity is significant, and the timeline is shorter than most people realize.
#AI Identity Management
#CIAM
#AI Agents
#Non-Human Identities
#Identity and Access Management (IAM)