Secure AI Agent Identity in Private Cloud and Hybrid Environments

Identity for AI is now available in self-managed software, so enterprises that need to run identity in private-cloud, hybrid, air-gapped, and/or regulated environments can secure AI agents. Built for organizations that need agentic identity and access management (IAM) deployed in controlled environments, this update enables on-premises, private-cloud, and hybrid ecosystems to secure, govern, and audit AI agents.

Many large enterprises still run critical identity infrastructure in private-cloud, hybrid, air-gapped, or tightly regulated environments. That is a deliberate choice, tied to data residency, sovereignty, and control requirements they cannot walk away from.

These organizations do not run software on-premises because they are behind. They run it because a regulator, a data-residency mandate, or a security policy requires tier-zero infrastructure, including the identity layer, to stay inside a boundary they control. Moving that layer to a SaaS model is often not an option, no matter how capable the service is.

Now these teams face the same pressure everyone else does. AI agents are entering their systems, and those entities need to be secured as real identities with their own credentials, scopes, and audit trails. The gap is that most Identity for AI providers in the industry are strategically cloud-only, which leaves many enterprises without a clear path to enable AI agents to interact with their resources securely.

90% of developers regularly used AI tools for coding or development in January 2026.¹ Agents are moving out of pilots and into production workflows that touch regulated data, core banking systems, patient records, and industrial controls. Once an autonomous actor can retrieve data and take action inside those systems, it becomes an access-control problem, not an experiment.

For enterprises with high security and regulatory requirements, that shift arrives with a constraint others do not carry. They cannot simply adopt a cloud-only agent-security product and route sensitive traffic through it. The controls have to sit inside an environment they control and can assure, or they do not meet the mandate at all.

Waiting is not a comfortable option either. Teams that stall on securing and managing AI agents' access to resources will rapidly accumulate unmanaged automation, static credentials, and over-provisioned service accounts that are hard to unwind later. Bringing agent controls through a self-managed software option lets these enterprises adopt automation on their own terms, inside the boundary their compliance obligations already define, instead of choosing between innovation and control.

Identity for AI capabilities are now available in Ping's self-managed deployment option, Ping Advanced Identity Software. Enterprises that opt to host their identity stack in on-premises and private cloud environments can secure autonomous actors with the same controls Ping brings to its SaaS Identity for AI deployment options.

This is part of Ping Identity's broader Identity for AI solution. The trust model behind it, runtime identity, moves the security decision to the moment of action, so access is evaluated continuously as an agent works rather than once per session.

Now, with Ping Advanced Identity Software, enterprises get the same core controls that define this approach, but they can deploy them where they choose instead of being forced to use a SaaS product. That includes registering agents as first-class identities, assigning owners, managing delegated permissions, and maintaining centralized lifecycle control across onboarding, changes, and deprovisioning. The deployment model changes, but the identity approach does not. A private-cloud bank and a cloud-native retailer can now hold their autonomous actors to the same standard of trust, ownership, and auditability.

Just as they are in Ping's SaaS deployment options, in self-managed software, each AI agent is treated as a first-class identity rather than a generic bot, script, or service account. Agents get their own credentials, their own scopes, and their own audit trail, which keeps their activity distinct from the human accounts around them.

That approach rests on three practical capabilities. Together they cover how agents are trusted, how they are managed over time, and how their access to tools and resources is controlled at runtime.

Agent guardrails through delegation and token exchange

Agent IAM Core models agents as distinct OAuth 2.0 identities, each with its own delegated privileges. This uses delegated access rather than impersonation, so an agent acts with defined, limited authority instead of borrowing a person's credentials.

Token exchange lets an agent carry the right scope for a given task and nothing more. Because delegation is explicit and granular, the resulting chain is auditable, and sensitive actions can still require human approval before they proceed. That last point matters in regulated settings, where a person often needs to stay accountable for what an automated actor does on their behalf.

Agent observability and lifecycle management

Agent IAM Core also treats AI agents as first-class identities instead of burying them among generic OAuth clients, which gives teams that build and deploy agents a clearer way to see what each agent is, what it was approved to do, and how it is acting over time. That visibility matters when agents are created dynamically or scaled across environments, because security teams need to distinguish agent activity from human users and conventional applications in logs, audit trails, and administration workflows.

AI-agent-specific Dynamic Client Registration (DCR) also gives organizations a more controlled way to onboard and manage agents throughout their lifecycle. With policy applied at registration, teams can define how agents come in, what grant types and scopes they can use, and whether delegated access is allowed from the start. The result is a more auditable, manageable model for agent identity at scale, helping organizations keep fast-growing agent ecosystems visible, constrained, and easier to trust in self-managed environments.

Model Context Protocol and resource safeguarding

Agent Gateway acts as a Model Context Protocol (MCP) security gateway. It validates MCP requests, audits both requests and the actors behind them, throttles rates, enforces OAuth and fine-grained access policies, and transforms tokens at the point where agents connect to tools. Agent Gateway intercepts every incoming call and applies runtime authorization, all the way down to the tool level.

It provides audit-ready visibility into what an agent did, which matters most when an MCP server exposes real systems and data. In practice, this is the enforcement point where an autonomous request either meets policy or gets stopped before it reaches a protected resource.

The agent controls are new, but the foundation underneath them is not. Identity for AI in software runs on the same hardened, standards-based platform enterprises already operate, which means these capabilities arrive as an integral part of a proven enterprise-grade identity platform rather than a new industry player to vet.

That distinction carries real weight for regulated buyers. Adopting a brand-new platform means fresh security reviews, new integration work, and new operational risk. Adopting a continuously evolving platform with sustained investment—and an established reputation as an industry leader—lets teams add agent controls while keeping the performance, compliance, and operability characteristics that have consistently been proven over time.

These updates run across the broader self-managed platform, from federation to identity lifecycle and directory services, so the following capabilities support these controls without asking teams to adopt a new platform.

Performance, scale, and operations

With distributed tracing now supported across most Ping Advanced Identity Software components, teams gain full end-to-end observability to trace and debug requests seamlessly. This capability is paramount for Identity for AI, where autonomous agents introduce high-volume, dynamic access patterns, requiring deep request-level visibility to maintain control, trace delegated identities, and resolve issues instantly.

Security hardening and compliance depth

Recent releases add support for AI agents alongside Federal Information Processing Standards (FIPS) 140-3 support. That combination matters for organizations that must meet strict cryptographic requirements while adopting automation.

The platform stays aligned with established standards, including FIDO, FAPI, and FIPS. For regulated customers, that alignment is what lets them extend to new actors without stepping outside their compliance obligations. It is the difference between a control that satisfies an auditor and one that creates a new finding.

Standards and interoperability

Token exchange and audience-aware token handling support agent flows, so a token issued for one purpose is not silently reused for another. This keeps delegation precise as agents move between services.

Recent releases add audience-based introspection for exchanged tokens, and OAuth enforcement remains standards-based for MCP protection. Building on open standards keeps these controls interoperable across a heterogeneous ecosystem, which matters because few enterprises will build all their automation on a single platform.

Ping Advanced Identity Software has also introduced support for ID-JAG and CIMD, extending these agent-security patterns across authorization domains. ID-JAG lets enterprises issue and consume identity assertions in token exchange flows, helping agents carry user-backed delegation across systems without repeatedly re-consenting or overextending a token beyond its intended audience. CIMD adds dynamic client metadata retrieval and validation at runtime, which is especially valuable for AI agents and other short-lived or externally originated clients that are impractical to preregister one by one. Together, these additions strengthen interoperable, standards-based trust for agent ecosystems while reducing brittle, manual client setup.

Administrative simplicity and modernization

File-based configuration is supported in production, which makes it easier to manage identity infrastructure as code and keep environments consistent. That fits the automation practices these teams already use.

These recent releases also bring DCR scripting improvements for managing agent identity attributes. Administrators get finer control over how agents are registered and shaped, without adding manual steps that slow deployment or invite error.

These updates also add OAuth client tagging with a clear Identity for AI purpose: it lets administrators distinguish AI agents, MCP servers, workforce apps, and other client types instead of treating every OAuth client as a generic application. That matters in agentic environments because secure AI operations depend on knowing what kind of actor is requesting access, relating that actor to the right trust model, and enforcing the right controls at runtime. With Tag Management, admins get a dedicated menu for managing default and custom tags, allows one or more tags to be assigned to OAuth clients, and exposes a Tag Authentication Selector so policies can branch differently for AI agents, MCP servers, or conventional apps. The result is a more governable agent ecosystem: teams can classify clients more cleanly, filter them more easily, and apply least-privilege, context-aware policy decisions that better fit Identity for AI use cases.

For the agentic enterprise, this closes a real gap. They can secure AI agents with delegated access, runtime enforcement, and audit-ready visibility while keeping identity infrastructure under their own control.

It also changes the internal conversation. Instead of arguing whether to relax a deployment mandate to adopt agent security, leaders can pursue automation and keep the architecture their compliance posture depends on. Security and enablement stop being a trade-off.

The benefits are concrete for the teams making these decisions:

• Secure autonomous actors without giving up deployment control or moving to a cloud-only model.
• Support private-cloud, hybrid, and air-gapped deployment options with one consistent set of controls.
• Meet management, observability, and standards requirements, including FIPS 140-3 and OAuth-based enforcement.
• Keep innovating on a trusted software platform where Identity for AI is an integral part.

Few vendors can credibly offer Identity for AI as self-managed software. For IAM and platform architects, that means these controls fit an existing architecture instead of forcing a new one, and the roadmap for humans, service accounts, and autonomous actors stays on a single foundation.

Enterprises that want to run tier zero infrastructure as self-managed software are not left behind in the AI era. The controls that let enterprises trust, manage, and audit agents on-premises and in private clouds are now available from Ping Identity.

As agents take on more work, every action, interaction, and request needs to be evaluated in real time by a scalable identity fabric. Ping Identity's approach gives private-cloud and hybrid enterprises a way to meet that shift on their own terms.

For the full details behind these updates, see the latest release notes for each component:

• PingAM
• PingIDM
• PingDS
• PingFederate
• PingAccess
• PingDirectory

1 JetBrains Research