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Covering the latest thought leadership, events, and news about identity security
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Most organizations understand that scaling AI securely requires more than selecting the right platform or iauditabilityromising use cases. As AI becomes embedded in business processes, access to systems, data, and applications must be governed just as carefully as the technology itself.
The question is no longer whether identity matters. The question is whether the organization has the controls, visibility, and governance needed to scale AI securely.
As explored in Why Identity Must Come Before AI, identity provides the foundation for governing access, accountability, and trust as AI adoption expands.
In reality, governance frameworks, policy reviews, and identity assessments are rarely the first steps in an AI initiative. More often, organizations start by trying to solve a business problem. A team enabled a copilot to improve productivity. A developer integrated AI into an application. An operations team automated a repetitive task. None of these decisions are inherently risky.
The challenge is that AI adoption rarely remains limited to a single use case. What begins as experimentation can quickly expand across teams, systems, and data sources, often faster than governance can keep pace.
By the time security, identity, and risk teams are asked to weigh in, AI is often already connected to business processes, enterprise applications, and sensitive data sources. At that point, the challenge is no longer whether AI should be used. It's determining how to govern it effectively.
Is your organization actually ready to scale AI securely?
Answering that question requires more than evaluating models, platforms, or infrastructure. Leaders need visibility into how AI is being used, what it can access, who owns it, and how those interactions are governed. They need a way to address shadow AI, govern AI agents and non-human identities, and establish controls that can scale alongside adoption.
An organization’s readiness to securely adopt and scale AI depends on more than its technology, platforms, infrastructure, data quality, and potential use cases. It also depends on whether the organization has the visibility, governance, and identity controls needed to manage AI as adoption expands.
A practical readiness assessment should answer questions such as:
These questions quickly expose two common gaps: shadow AI and poorly governed non-human access. Organizations looking for a structured way to evaluate these gaps can begin with an Identity-First AI readiness assessment focused on identity, governance, and access controls.
Enterprises can govern shadow AI by gaining visibility into the tools employees are using, understanding what data those tools can access, and establishing clear policies for responsible use. Shadow AI emerges when employees adopt AI tools without formal review, approval, or oversight. This can include public AI platforms, unsanctioned copilots, AI browser extensions, or AI capabilities embedded within existing software.
In most cases, employees are not intentionally bypassing policy. They are trying to solve problems faster than existing governance processes can respond. That makes shadow AI as much a visibility and governance problem as a user-behavior problem. Gartner notes that employees are increasingly accessing generative AI platforms from corporate systems, creating policy, governance, and data protection challenges that organizations must address through visibility, monitoring, and education initiatives.1
Without visibility into how AI is being used, organizations cannot effectively assess risk or establish appropriate controls. It becomes difficult to answer basic questions such as:
These are basic identity questions. What changes with AI agents is the speed, autonomy, and number of actions they may perform once access is granted.
Banning AI is not a governance strategy. A more practical approach is to define approved tools and use cases, set clear rules for sensitive data, and make it easier for employees to understand what is permitted.
Traditional IAM governance has focused primarily on human users, applications, and relatively predictable machine identities. Access requests followed established approval processes. Employees logged into systems, performed assigned tasks, and participated in periodic access reviews. Governance models assumed clear relationships among users, applications, and data.
AI is expanding the number and autonomy of non-human actors, challenging governance models that were built around more predictable patterns of access and activity. A marketing team may use AI to summarize customer feedback. A developer may rely on an AI assistant to accelerate software development. An operations team may deploy automation that interacts with multiple systems simultaneously. In each case, AI may access information and perform actions with greater speed, scale, and autonomy than many existing governance processes were designed to handle.
As AI adoption expands across the enterprise, governance programs must move beyond workforce identities alone and account for machine identities, service accounts, APIs, automation platforms, and AI agents operating across the environment.
Traditional governance is not obsolete. It simply needs to evolve to account for the machines, services, and AI agents increasingly carrying out work across the enterprise.
The best approach is to treat AI agents, machine identities, and non-human accounts as first-class citizens of the identity program, with defined ownership, least-privilege access, approval requirements, ongoing monitoring, and lifecycle management. AI agents access systems, retrieve information, make recommendations, and increasingly take action on behalf of users. Yet many organizations deploy these capabilities without the same governance requirements applied to workforce identities.
For every AI agent, organizations should be able to answer:
This approach extends identity lifecycle principles to AI agents, service accounts, workload identities, and other non-human identities, while also governing the credentials they use, such as API keys and certificates.
Without clear ownership and governance, organizations risk creating a rapidly expanding population of privileged non-human identities that become difficult to monitor and even harder to control.
Before enterprises can scale AI responsibly, they need visibility into how it is being used. That means understanding which AI tools are in use, what data they can access, which identities they rely on, and what actions they are performing across the environment.
Visibility should connect AI use to the identities, data sources, systems, and actions involved. Once that is understood, organizations can apply ownership, access reviews, monitoring, and audit controls where they are actually needed. Organizations can then strengthen accountability through ownership models, access reviews, audit trails, and monitoring processes.
At the same time, access controls need to evolve beyond static permissions. AI environments benefit from:
Together, these controls make access decisions easier to explain, review, and change.
When employees hear the word "policy," they often assume new restrictions are coming. Effective AI governance should accomplish the opposite. Good policies provide clarity by helping employees understand how AI can be used responsibly while giving business leaders confidence that adoption aligns with organizational expectations and risk tolerance.
Common policy areas include:
The goal is not to create friction. The goal is to make the boundaries clear: what AI can be used for, what data it can access, and when additional review is required.
While every organization's AI journey is different, most successful programs follow a similar progression.
This gives organizations a repeatable way to move from isolated or experimental use cases to broader deployment without losing visibility or accountability.
AI adoption is accelerating, but scaling AI securely requires more than technology.
Business leaders need the visibility, governance, and identity controls necessary to manage AI consistently and at scale. That means addressing shadow AI, governing non-human identities, strengthening access controls, and establishing clear accountability across AI-enabled processes.
As AI use expands, the practical test is whether the organization can still explain what is being used, what it can access, who owns it, and how its actions are governed.
Ready to Assess Your AI Readiness?
MajorKey's Identity-First AI Advisory assesses AI readiness, identifies identity and governance gaps, and delivers a practical roadmap for scaling AI securely.
Next in the series: Preparing Your Identity Program for Agentic AI. Learn how identity programs must evolve to govern autonomous AI agents, manage decision authority, maintain auditability, and establish trust in AI-driven operations.
1 Gartner, CISOs Must Bring Shadow AI Into the Light, Andrew Walls, Jeremy D'Hoinne, John Watts, 4 July 2025, ID G00836600.
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Covering the latest thought leadership, events, and news about identity security
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