Saviynt, a cloud-based Identity Governance and Administration (IGA) platform, recently released its list of identity security trends and predictions for 2026. The report highlights the “Triple Threats” facing identity infrastructure: Agentic Risk, Governance Deficit and Visibility Gap. The traditional identity boundary is disolving and we are entering a new “identity” world order where Non-human identities (NHIs) and autonomous workflows redefine the security stack. Here are the key takeaways, insights and action steps for getting ready for the new “identity” order.
AI adoption proliferation led to a dangerous gap between employees adopting autonomous agents and copilots, and security teams that lacked the frameworks to govern them. The report outlines why identity security is no longer just a support function and is instead the essential foundation for AI growth.
New World Order
- Non-human identities (NHIs) and autonomous workflows redefine the security stack.
The “Triple Threat”: The traditional boundaries of the network have dissolved
Agentic Risk:
AI agents act with administrative privileges that often exceed those of their human creators.
The Governance Deficit
Organizations are struggling to govern machine-speed identities using human-speed manual processes.
The Visibility Gap
Most leaders cannot identify how many autonomous agents are currently active or what data they are accessing.
The above threat requires a fundamental re-engineering of how we verify and monitor access.
Trend 1: Bad Actors Will Target AI Identities
We used to build security around the human person, but a new resident has moved into the network: the AI agent. These non-human identities often operate with elevated administrative privileges – sometimes exceeding the authority of the individuals who created them. Yet they frequently oversee and manage the lifecycle controls applied to human accounts.
Key Insights:
- Most organizations can’t say how many agents are running or what decisions they are making.
- Attackers use prompt injection and model manipulation to turn agents into insider threats.
- AI identities require the same governance rigor that organizations have spent decades building for human users.
Priority Actions
- Treat identity as infrastructure by integrating non-human identities (NHI) into the core security stack.
- Centralize lifecycle management to provide a single view of both human identities and NHIs.
- Implement dynamic privilege enforcement and continuous monitoring as baseline requirements for autonomous workflows.
Trend 2: MCP Will Accelerate and Secure AI Innovation
MCP creates a standard way for AI agents to connect directly to applications, tools, and data sources across the enterprise. It plays a role for autonomous systems similar to what APIs once did for cloud platforms. Instead of routing work through a human, MCP allows machines to work with machines.
The Model Context Protocol (MCP) is an open-source standard created by Anthropic in late 2024 to enable AI models to connect securely with external data sources and software tools. It acts as a universal bridge, allowing AI to read local files, access databases, and use developer tools like GitHub
An MCP connection carries real authority. It allows an agent to retrieve data, trigger workflows, and act inside critical systems without a person in the middle. When those connections are poorly governed, they become high-value access paths. If compromised, they offer attackers a way to influence trusted systems at machine speed and largely out of sight.
In 2026, machine-to-machine dialogues will become the new frontier of risk.
Key Insights
By removing the person in the middle, we remove the pause where judgment once lived. MCP tokens, credentials, and access rules are becoming primary targets because they enable agents to operate within critical systems at machine speed.
Priority Actions
• Treat MCPs as part of the identity surface, not as an application detail.
• Bring MCP under the same governance disciplines as any privileged access path, including strong authentication, clearly defined scopes, least-privilege enforcement, and continuous monitoring of how agents are using it.
Trend 3: Data Security Will Return as a Frontline Challenge
Key Insights
- AI does not distinguish between what it can access and what it should access, making data security a frontline challenge.
- AI is a master of correlation. A file abandoned years ago is no longer buried; it is a single prompt away from being seen by the entire company.
- When an agent acts, it inherits the permissions of its creator, including both intentional and accidental permissions. This turns every instance of excess privilege into an instant exposure.
Priority Actions
Clean your data. That means:
- Improve identity data hygiene
- Tighten access controls
- Enforce least privilege
- Establish clear ownership over who and what can access sensitive information.
Trend 4: Breaking Down Siloes and Moving Toward Zero Trust
Most organizations still rely on a collection of tools that lack sufficient context sharing. That may have been manageable when threats moved at human speed, but it becomes a problem when attackers use automation and AI to probe, pivot, and escalate faster than teams can respond. Fragmented security transitions from a manageable liability to a critical risk.
Key Insights
- As organizations adopt agentic AI, the ability to enforce just-in-time access and least privilege becomes essential to maintaining control.
Priority Actions
- Identity is the common layer that connects disparate systems. Shift toward a unified control plane where identity context flows freely into detection and response systems.
- To manage agentic AI, implement just-in-time access controls to ensure permissions are active only when necessary.
- Prioritize interoperability between security platforms to fulfill the zero-trust requirement of verifying every action across the entire environment.
Trend 5: AI Brings Identity to the Center of Cybersecurity
Identity is the domain that determines who or what is taking an action and whether that action should be allowed. While AI creates new risks, it is also the only way to provide the scale required to solve the governance problems it has accelerated.
Key Insight
- Identity is no longer just a gatekeeper; it is the strategy that enables safe, efficient AI adoption.
- Organizations that treat identity as infrastructure will scale AI safely; those that treat it as a compliance exercise will struggle to maintain control.
Priority Actions
- Leverage AI tools to address long-standing governance gaps, such as identifying orphaned accounts and classifying complex access patterns.
- Move identity management from a compliance-focused exercise to a central architectural role that governs all automated and human workflows. Building this foundation is necessary to scale AI innovation without increasing organizational risk.
In the AI era, identity isn’t just supporting your security strategy – it is the strategy. By integrating non-human identities, unifying visibility, and enforcing adaptive security, organizations can build a system of trust that scales with the speed of innovation.
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