AI/ML , AI benefits/risks BrandView Secure AI agent identity in private cloud and hybrid environments August 21, 2026 Share By Saira Guthrie (Adobe Stock) 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. Where SaaS-Only Identity Leaves Enterprises Behind 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. Why Enterprises Feel the Pressure to Get Agentic Identity in Place Immediately 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 Now Extends Across Deployment Models 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 . What Identity for AI Looks Like in Self-Managed Software 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. Traditional service-account approach First-class agent identity with Agent IAM Core Agents share static credentials or reused service accounts. Each agent is a distinct OAuth 2.0 identity with its own privileges. Access is inherited or over-provisioned by default. Access is delegated with defined, limited scope for each task. Actions are hard to trace back to a specific actor. Delegation chains are explicit and auditable end to end. Onboarding and cleanup happen manually and inconsistently. Dynamically registers, rotates, and retires agents across the lifecycle. 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 Trusted Software Foundation Behind the AI Story 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