There is something almost theatrical about the way the technology industry talks about artificial intelligence (AI) right now.
Every product launch is framed as a breakthrough. Every conference agenda leads with it. Every executive conversation, no matter where it starts, eventually circles back to the same gravitational pull: what does AI mean for us and how quickly can we move?
That excitement is not misplaced. AI is genuinely changing what is possible with identity security. The ability to analyze access patterns at scale, detect behavioral anomalies in real time, surface risky entitlements across massive identity landscapes — these are meaningful capabilities. Identity teams that have spent years drowning in manual certification campaigns and spreadsheet-driven access reviews are understandably ready to believe that something is finally going to make their lives easier.
But somewhere in the rush, a quiet assumption has started to take root. We are beginning to talk about AI as if it arrives like a rescue team, ready to solve problems that identity programs have been struggling with for years.
It doesn’t.
And the organizations that discover this the hard way are going to do so in the worst possible moments.
Anyone who has spent time around theater or film understands something that gets lost in technology marketing: a star cannot carry a production alone.
The leading actor gets the spotlight. The reviews mention their name. The promotional material is built around their presence. But a great performance is always the product of an entire production working in concert. The supporting cast creates the conditions that let the lead actor actually succeed. The stage manager keeps everything running. The set design gives the story a world to exist inside. The director ensures all of it comes together for a cohesive experience.
Strip away those supporting elements and even the most talented performer looks lost.
AI is currently the leading actor in the technology conversation. That is not a criticism. The role is deserved. But identity security is the stage and right now, not enough people are paying attention to the condition of the boards beneath the lights.
Because here is what is true regardless of how sophisticated the AI becomes: Every intelligent system still operates through identities.
Every AI agent that interacts with your infrastructure needs credentials to do it. Every automated workflow that touches sensitive data is doing so through access relationships that someone has to govern. Every decision the system makes is only as trustworthy as the identity data it is working from.
If that foundation is fragile, AI does not strengthen it. It accelerates the consequences of it being fragile in the first place.
Every good story begins with someone establishing the world. Before the audience can invest in what happens, they need to understand where they are.
In identity security, that role belongs to posture management.
Identity posture management answers the questions that should be answered before any organization starts layering intelligent automation on top of its environment. How many identities exist? Where do they live? What access has accumulated over time and which of those access relationships create unnecessary risk? Are entitlements aligned with what people actually need to do their jobs or have they simply grown, unchecked for years?
Most organizations significantly underestimate the complexity of their own identity landscape. Identities exist across employees, contractors, partners, service accounts, application workloads, APIs and increasingly, AI-driven agents that operate with varying degrees of autonomy. Each of those identities carries access relationships and those relationships form the organization’s actual security posture, whether or not it can clearly see them.
AI can analyze that environment with remarkable sophistication. It can identify patterns that no team of human analysts could find manually, surface toxic privilege combinations and flag behavioral anomalies that traditional tools would miss entirely. But none of that works if the underlying identity data is fragmented, stale or poorly maintained.
“Garbage in, garbage” out is a technology principle that has been true long before AI was part of the conversation. It’s still true because the systems doing the analysis are now more powerful. If anything, it matters more because the outputs of AI-driven analysis get acted on at machine speed.
Identity posture management gives intelligent systems something meaningful to work with. Without it, AI isn’t analyzing your environment. It’s analyzing your assumptions about your environment, which is a very different thing. Identity posture management is the foundational layer that gives intelligent systems, such as AI and machine learning models, something truly meaningful and accurate to work with. It provides a real-time, validated and holistic understanding of your entire identity landscape, including users, machines and services, along with their entitlements, access rights and risk profiles.
Without robust identity posture management, AI is not analyzing the objective reality of your environment; it is merely analyzing your assumptions about your environment. This is a crucial distinction with profound security implications.
Your environment’s actual security posture is often a complex, shifting target, riddled with legacy access, excessive privileges and toxic combinations that may be hidden from manual inspection or static security tools. If the AI is only fed data reflecting your known policies and configurations — your assumptions — it will naturally miss the critical, nuanced and often highly dangerous deviations from that ideal state. It will optimize for the assumed environment, not the actual environment.
This gap leads to flawed security insights, inaccurate risk scoring and automated responses that are ineffective or, worse, detrimental. By providing the AI with a continuously verified identity posture — a single source of truth detailing who has access to what, why and the inherent risk — you enable the system to move beyond merely auditing policy compliance to proactively predicting and preventing identity-centric threats.
If posture management provides the context, identity management provides the infrastructure that the entire program depends on.
Identity management governs the lifecycle of identities inside the organization. It determines how identities are created, how access is assigned at the moment of onboarding, how permissions evolve when roles change and how access is removed when it is no longer needed. This is work that has been central to identity programs for decades. It’s also work that’s significantly harder than it used to be.
The reason is straightforward: the identity surface has expanded far beyond what traditional identity management was designed to handle.
For most of the history of enterprise IT, identity management primarily meant managing employees. Today, it means governing a sprawling population that includes external identities, as well as automation services, application workloads, third-party integrations, APIs and an accelerating number of AI agents that interact with enterprise systems continuously and often without direct human involvement. In many modern environments, non-human and AI identities already outnumber human users by a significant margin.
That shift matters because non-human identities and AI agents operate differently. They do not log out at the end of the day. They interact with multiple systems simultaneously. They execute actions at machine speed. And when their lifecycle governance is weak, the consequences of that weakness scale just as fast as the systems themselves.
An AI agent operating inside a poorly governed identity environment does not pause to check whether its privileges make sense. It simply uses them. If those privileges are excessive, stale or improperly scoped, the blast radius of anything that goes wrong expands accordingly.
This is why the organizations that will extract real value from AI in their identity programs are the ones that have done the foundational work on lifecycle governance, not just for their human workforce, but for every identity that touches their environment.
Behind lifecycle governance sits another set of capabilities that determine how identities actually interact with systems in practice.
Each of these capabilities solves a distinct problem. Together, they form the controls that make policy real.
As AI systems begin interacting with enterprise infrastructure more frequently and autonomously, these controls become critical in ways they were not before. An AI agent can initiate actions across multiple systems simultaneously. It can operate around the clock without fatigue, hesitation or the natural friction that comes from human behavior. It can escalate privileges, traverse system boundaries and generate downstream effects that move far faster than any review process can track.
Access management ensures that those actions happen within defined boundaries. It determines which systems AI can reach, what it can do when it gets there and how its behavior is monitored over time. Strong access controls are not a barrier to AI effectiveness. They are the guardrails that make AI trustworthy enough to actually use.
Remove those guardrails and you haven’t built an intelligent system. You’ve instead built a very fast, very capable engine with no steering.
There is an old storytelling device called deus ex machina. The term comes from ancient theater, where complex plots that had written themselves into impossible corners were sometimes resolved by lowering a god onto the stage from above, arriving at the last moment to untangle everything the story had made impossible to fix.
It’s a satisfying device in the short term. The audience gets a resolution. The tension breaks. But it’s generally considered poor storytelling because it does not earn its resolution. The problem was not actually solved. It was bypassed.
A version of this is quietly shaping how some organizations are thinking about AI in their identity programs.
The hope — sometimes expressed directly and sometimes just implied by how AI tools are being marketed — is that AI will descend onto the existing environment and clean up what years of accumulated complexity have left behind. That it will rationalize entitlements, untangle toxic access combinations and bring order to identity landscapes that have grown increasingly difficult to manage manually.
That is not how it works.
AI does not fix a broken identity program. In many cases, it simply makes the brokenness visible faster and makes the consequences of that brokenness more consequential. Because when automation is running on top of fragile foundations, it tends to amplify the fragility.
The organizations that are extracting real value from AI in their identity programs are not the ones that deployed AI and hoped it would fix everything. They are the ones that built strong posture management, disciplined lifecycle governance and consistent access controls and then layered AI on top of that foundation to do things that would otherwise be operationally impossible.
The sequence matters. AI builds on identity security. It does not replace it.
None of this is an argument against AI. The tools are real, the capabilities are meaningful and the potential impact on identity programs is significant.
AI can analyze access relationships across environments so large and complex that manual governance would simply be impossible. It can detect behavioral anomalies in identity data at a speed and scale that transforms what risk detection actually looks like. It can automate workflows that have historically consumed enormous amounts of manual effort and turned identity reviews from compliance exercises into genuine risk management.
That kind of capability can change the trajectory of an identity program.
But the key to unlocking it is remembering something that gets lost when a new technology arrives with enough momentum to reshape an entire industry’s conversation: the fundamentals do not disappear because the tools improve. They become more important.
Identity posture management gives AI a clear picture of the environment. Identity lifecycle governance ensures that the identities AI is operating on are accurate, appropriately scoped and properly maintained. Access management enforces the boundaries that make AI behavior trustworthy rather than simply powerful.
Those three things together are the stage. AI is the performance. And while the audience is watching the star, the reason any of it works is what was built before the curtain went up.
Your organization is probably already in the middle of figuring out what AI means for your security program. That’s the right conversation to be having. Just make sure the conversation includes the one that has always mattered more:
Who has access to what, does it make sense that they have it and do you actually know?
Everything else depends on the answer.
See how Saviynt is putting AI to work for identity security with Zuma