Part 3 of 3

By Vidyaa Ganesh

This is Part 3 of a three-part series on IAM maturity measurement. Part 1 reviewed the published research showing that 60-70% of organizations remain at early-to-mid stages of IAM maturity. Part 2 examined the frameworks currently available and the structural reasons no standard has emerged. This final installment proposes what a credible standard would need to include.

If the IAM community is going to move toward standardized measurement, what would a credible framework need to look like? Based on the published research reviewed in this series, established precedent from other domains, and the practical needs of practitioners, seven design principles emerge.

Principle 1: Domain Decomposition, Not Monolithic Scoring

IAM is not one thing. A useful maturity framework must decompose the discipline into distinct domains and assess each independently before producing any aggregate score. Treating IAM as a monolith obscures critical gaps. An organization with excellent identity governance but absent privileged access controls has a fundamentally different risk profile than one with moderate capabilities across the board, even if their aggregate scores are identical.

Gartner’s six-dimension structure is a reasonable reference point for domain decomposition. At minimum, a framework should separately assess identity lifecycle management, access management and authentication, privileged access management, and governance. Depending on the organization’s context, customer identity, cloud identity, and identity threat detection may also warrant distinct assessment.

Principle 2: Foundational Prerequisites Must Gate Advanced Claims

This may be the single most important design principle. A framework must account for the reality that some IAM capabilities are prerequisites for others. An organization that has deployed sophisticated analytics and AI-driven threat detection but has not implemented basic multi-factor authentication on privileged accounts has a fundamentally flawed security posture. A credible maturity score should reflect that.

This principle is well-established in adjacent fields. CMMI’s staged representation explicitly requires lower-level process areas to be satisfied before higher-level ratings can be claimed. SailPoint’s Horizons framework states that to be placed in a given horizon, capabilities must cover most environments and identity types. CISA’s Zero Trust Maturity Model states that identity pillar capabilities must be established before device trust can be meaningful.

A standardized IAM framework should identify a limited set of foundational controls and ensure that gaps in those controls are reflected in the overall maturity score. Without this, organizations can game the assessment by investing in visible, advanced capabilities while neglecting the basics that actually prevent breaches.

Principle 3: Risk-Weighted Scoring

Not all IAM controls carry equal risk. The presence or absence of MFA on privileged accounts has a materially different security impact than the presence or absence of a formal identity data classification scheme. A credible framework must reflect this through some form of risk-weighted scoring.

Published research supports this approach. IBM’s 2025 data shows that organizations with extensive security automation experienced average breach costs of $3.62 million compared to $5.52 million without, a 34% cost reduction attributable to mature controls. Microsoft’s Digital Defense Report found that MFA blocks over 99% of account compromise attacks. The data makes clear that certain controls deliver disproportionate risk reduction and should be weighted accordingly.

Principle 4: Industry-Contextualized Benchmarks Derived from Empirical Data

A maturity score in isolation is nearly useless. What makes it actionable is context: how does this score compare to others in the same industry, of similar size, facing similar regulatory requirements? A credible framework must include an empirical benchmark layer.

The data for this already partially exists. SailPoint publishes industry breakdowns. Ponemon publishes breach cost data by industry. Simeio publishes maturity scores by vertical. What does not exist is a single, consistent benchmark set that covers all IAM domains across major industries. Building this requires either a large-scale survey effort or, more practically, the aggregation of assessment data across organizations over time, with appropriate anonymization and consent.

Principle 5: Transparency of Methodology

Every number in a maturity assessment should have a documented derivation. If a domain receives a higher weight than another, the rationale should be published. If a benchmark is based on a specific data source, that source should be cited. If a benchmark is estimated rather than measured, that should be disclosed.

This level of transparency is uncommon in commercial assessment tools, where scoring logic is often proprietary. But for a community standard, it is essential. Consultants need to be able to explain and defend the numbers they present to clients. CISOs need to trust that the methodology is sound before presenting results to their boards. Transparency is not just a nice-to-have. It is a prerequisite for adoption.

Principle 6: Technology Agnosticism

A standardized framework must assess capability, not tooling. The question is not whether an organization has deployed a specific vendor’s IGA platform, but whether it has a functioning identity lifecycle management process that covers joiners, movers, and leavers with appropriate automation and oversight. This distinction matters because organizations achieve similar maturity outcomes with very different technology stacks, and a useful standard must accommodate that diversity.

Principle 7: Actionable Outputs

A maturity assessment that produces only a number is insufficient. The assessment process should produce outputs that enable direct action: identification of specific capability gaps, prioritized remediation guidance, regulatory compliance mapping, and clear criteria for what moving from one level to the next requires.

Gartner’s recommendation of outcome-driven metrics for IAM aligns with this principle. The goal of measurement is not the score itself but the improvement roadmap it enables. A framework that scores but does not guide action is an academic exercise.

Gaps and Open Questions

Even with sound design principles, significant gaps remain that the community will need to address.

Non-human identity. Machine identities (service accounts, API keys, certificates, workload identities) now outnumber human identities by ratios that CyberArk’s 2025 research places at approximately 80 to 1. Yet most maturity models treat identity as synonymous with human identity. A credible standard will need to account for non-human identity management as a distinct assessment domain.

AI agent governance. As AI agents increasingly operate autonomously within enterprise environments, the question of how to govern their identities, permissions, and access patterns is emerging as a new challenge. SailPoint’s 2025 Horizons research added AI agent governance to its capability thresholds. No existing maturity model addresses this domain in depth. Any framework designed for longevity should be extensible enough to incorporate it.

The data collection problem. The most practical path to building reliable industry benchmarks is through the aggregation of anonymized assessment data across many organizations over time. This creates a chicken-and-egg problem: organizations are reluctant to contribute data to a benchmark pool unless the benchmark already exists, and the benchmark cannot exist without contributed data. Solving this will likely require a combination of opt-in data sharing, strong privacy guarantees, and a trusted neutral party to manage the aggregation.

Weighting validation. Any risk-weighted scoring system involves judgment calls about how much weight to assign to different controls and domains. These weights can be informed by published research on breach costs, attack frequency, and control effectiveness, but they cannot be fully derived from first principles. Ongoing validation through correlation analysis between assessment scores and actual security outcomes is needed to refine the weights over time.

Conclusion

The IAM community has a measurement problem. Five independent research sources, covering over 2,000 respondents, consistently show that 60% to 70% of organizations are at early-to-mid stages of IAM maturity. At the same time, the industry relies on a fragmented set of incompatible frameworks that prevent meaningful comparison, benchmarking, or progress tracking.

This is not a technology problem. The tools exist. This is a standards problem. The community lacks a shared, vendor-neutral, empirically-grounded framework for measuring IAM maturity, and the absence of such a standard has real consequences: CISOs cannot articulate their posture to boards in comparable terms, consulting firms deliver assessments that start from scratch with each engagement, and the industry has no aggregate data pool that could identify systemic weaknesses and drive collective improvement.

The design principles outlined in this series are not radical. Domain decomposition, foundational prerequisite gating, risk-weighted scoring, empirical benchmarks, methodology transparency, technology agnosticism, and actionable outputs are all well-supported by existing research and established practice in adjacent disciplines. What is missing is the will to build and adopt a standard that incorporates them.

Identity practitioners are the people best positioned to solve this. They live the problem daily. They understand the domains. They see the consequences of unmeasured and unmeasurable IAM programs. The question is whether the community will continue to tolerate a landscape where every assessment is a one-off, or whether it will converge on a shared approach that raises the bar for everyone.

About the Author

Vidyaa Ganesh is a Senior IAM Engineer and Team Lead at Raah Technologies, with over six years of experience spanning enterprise IAM implementations, strategic advisory, and maturity assessments. She has held consulting roles at KPMG Canada and Indigo Consulting, delivering identity governance and privileged access programs for financial services, energy, telecommunications, and public sector clients. She holds Okta and Saviynt certifications and a Master of Engineering from Concordia University. She is a member of IDPro.

Endnotes

17. Gartner, IAM Program Maturity Model (September 2025).

18. ISACA, CMMI Version 3.0: “A maturity level rating is achieved when all process areas at that level have been appraised as meeting their specific and generic goals.”

19. SailPoint, Horizons 2025-2026: “To be in one horizon, customer capabilities need to cover most environments and identities.”

20. CISA, Zero Trust Maturity Model (2023).

21. IBM Security, Cost of a Data Breach Report 2025.

22. Microsoft, Digital Defense Report (Microsoft Security, 2024).

23. NIST, SP 800-30 Rev 1: Guide for Conducting Risk Assessments (2012). See also FAIR Institute.

24. SailPoint, Horizons 2025-2026, Exhibit 7.

25. IBM Security, Cost of a Data Breach Report 2025, Industry Analysis section.

26. Simeio, State of Identity 2024.

27. Gartner, IAM Program Maturity Model (September 2025). Recommends outcome-driven metrics (ODMs).

28. CyberArk, The Urgent Reality of Machine Identity Security in 2025 (CyberArk, 2025), 2,600 decision-makers.

About the Author

Vidyaa Ganesh is a Senior IAM Engineer and a solutions architect with over six years of experience delivering identity governance programs for financial services, energy, telecommunications, and public sector clients. She holds a Master of Engineering from Concordia University, is a member of IDPro, and is the creator of AXIS (axis.identara.ca), an open IAM maturity assessment framework.

The post What Good IAM Measurement Looks Like appeared first on IDPro.

IETF 124, November 2024. Montreal was cold outside, the discussions inside were heated. AI agents were the topic on everyone’s lips, and every working group seemed to be looking at the problem through their own lens. The WIMSE folks were thinking about workload identity. The OAuth crowd was extending grants and tokens. SPIFFE was already deployed in production for service mesh identity. And the Agent2Agent BOF and WebBotAuth WG were trying to address the same topics through their own lens.

The identity community had spent the better part of two decades building a robust, battle-tested toolkit for authentication, authorization, delegation, and observability. OAuth 2.0, SPIFFE, WIMSE, Transaction Tokens, Shared Signals, OpenID Connect. This stuff works. It’s deployed at planet scale. And yet, every new “agent identity” proposal we were seeing started from first principles, reinventing token formats, credential lifecycle, even basic concepts like “what is a workload identity.”

Quickly we sat down with Pieter Kasselman, pointing at the elephant in the room: we don’t have a standards gap. We have an assembly gap. With the support of Brian Campbell, Nick Steele, Yaroslav Rosomakho, and Aaron Parecki, this thinking became draft-klrc-aiagent-auth, now at version -03. A personal Draft that introduces a simple but strong conceptual model called AIMS (Agent Identity Management System).

The thesis: agents are workloads

The core argument is deceptively simple: an AI agent is a workload. It needs an identifier. It needs credentials. It authenticates to other systems. It requests authorization. In short, something the industry has been solving for services, containers, and microservices for years.

Still, what makes agents operationally different is the risk profile:

Dynamic action surface. An agent doesn’t have a fixed API contract. It decides at inference time which tools to call, in what order, based on reasoning that didn’t exist when the agent was deployed.

Cross-domain by default. A single agent task routinely spans internal services, third-party APIs, other agents; each in different trust domains.

Time sensitive and high-churn. Agent instances spin up, fan out, terminate. Some are long lived, running for hours or days, most are short lived, running for seconds or minutes.

High-value targets. An agent with not-contextualized or not-bound credentials is essentially a privileged insider with no audit trail.

What AIMS actually does

AIMS defines a layered stack where each layer is built on guarantees from the one below. There’s no single solution to all of this: it takes a wide range of technologies covering identifiers, authentication, authorization, delegation, and monitoring. What didn’t exist before was the organizing framework that ties them together.

Here’s how it breaks down:

Identifiers. Foundation for everything. The draft proposes WIMSE identifiers (a superset of SPIFFE IDs). A URI that uniquely identifies the agent workload.

Credentials. Authentication and authorization rely on credentials that cryptographically bind identifiers to agents, instead of bare identifiers or long-lived secrets like API keys. The WIMSE credentials specification (X.509 certificates and Workload Identity Tokens) fits well here, along with SPIFFE JWT-SVIDs. Cryptographic proof of ownership of the identifier.

Provisioning. SPIFFE for the general case, while leveraging platform-specific mechanisms (MDMs for end-user posture assessment, cloud-native posture evaluation) depending on where the agent runs.

Authentication. Agents authenticate to LLMs, tools, services, resources, other agents. The draft maps WIMSE credential mechanisms with mTLS at the transport layer and HTTP Message Signatures, WIMSE Proof Tokens at the application layer. The through-line: avoid static pre-shared credentials, it’s an anti-pattern.

Authorization. Specifically, delegated authorization through OAuth. What an agent can access, whether it’s acting on behalf of a user or on its own, how end-users authorize agents to act on their behalf. The draft maps the existing OAuth specs to specific agent use cases: authorization code flow for delegated user flows, client credentials for autonomous agents, token exchange scope and authorization details management, identity chaining via ID-JAG for multi-domain workflows and leaves a space for policy based authorization which may be facilitated through AuthZEN and other policy based authorization standards

Observability and Remediation. Risk evaluation changes. Authorization may need to change mid-session. How do you propagate that? The OpenID Foundation’s prior work here is directly applicable: Shared Signals Framework (SSF), CAEP for continuous access evaluation, RISC for account-level signals. Audit critical details, revoke credentials, trigger re-evaluation.

Policy and Compliance. What are the rules? How do you measure against them? This is inherently deployment-specific. The draft deliberately doesn’t point to any single policy standard here, leaving room for organizations to fit their existing infrastructure in.

Every single component in that stack is either an existing RFC, a mature IETF draft, or an OpenID specification that’s already deployed in production somewhere. The draft doesn’t define any new wire protocol. It profiles and composes.

The constraints that motivated us

Through this work, we set ourselves a few constraints early:

If it exists and works, use it. Don’t reinvent credential formats. Don’t create yet another token type. Don’t define a new discovery mechanism when OAuth Server Metadata and Protected Resource Metadata already exist.

If something is genuinely new, call it out as a gap. The draft explicitly identifies areas where profiles, extensions, or new work is needed. Human-in-the-loop mid-execution authorization is one: CIBA gets close but the message flow and client initiation model don’t map well to an agent needing approval during execution, not before. That gap has already produced a new draft (more below). Cross-domain use of transaction tokens is another. Dynamic client relationship management proved DCR was not the end goal, the emergence of CIMD was helpful.

Don’t solve the deployment architecture. AIMS is a conceptual model, not a product spec. It might be one component or distributed across identity providers, authorization servers, policy engines, and runtime enforcement points. That was intentional as it has to map to real deployments that already look different from each other. 

The ecosystem is moving

This is the part that gives me optimism. The framework isn’t just a document; it helped catalyzing focused work on the specific areas where profiles and extensions are genuinely needed. It proved people aren’t reinventing but they’re extending precisely where the existing tire doesn’t fit the road.

The market started validating the thesis of the draft independently. Sean O’Dell, Distinguished Engineer at CVS Health and now a co-author on the Transaction Token Chaining Profile, published “You Already Have the Pieces. Now Build It.” on The Identity Underground. It’s worth reading in full. He opens with the same observation we built the draft around.

OpenAI, Anthropic, and Snowflake have all shipped Workload Identity Federation endpoints  using OIDC and SPIFFE-style credentials to authenticate workloads against their platforms without static API keys. This is literally the AIMS model landing in real systems. The theory is catching up to what practitioners are already doing, and vice versa.

Finally, new proposals have come out of this work:

OAuth Transaction Authorization Challenge by Yaroslav Rosomakho, Brian Campbell, Karl McGuinness, and Pieter Kasselman tackles head-on the human-in-the-loop gap we identified. CIBA’s message flow doesn’t map well to mid-execution approval as it’s client-initiated, not resource-driven. This draft flips it: the protected resource issues a signed challenge when a specific operation requires additional authorization; the agent relays the challenge to a client, which presents it to an authorization server that obtains approval and issues a transaction-scoped access token. It reuses OAuth’s existing capabilities.

Transaction Tokens for Agents by Ashay Raut extends the Transaction Token specification with agent-specific semantics through the act claim for identifying the agent performing the action, and an agentic_ctx claim for conveying operational constraints relevant to authorization, auditing, and policy evaluation within the service graph.

Transaction Token Authorization Grant Profile for OAuth Identity and Authorization Chaining by George Fletcher, Pieter Kasselman, and Sean O’Dell defines how a Transaction Token scoped to a single trust domain can be used as an authorization grant to obtain a JWT Authorization Grant for crossing a trust boundary. This is what makes cross-domain multi-agent workflows practical without requiring every authorization server to trust every other one directly.

WISE: Workload Identity Security Events by Dag Sneeggen, Sean O’Dell, Pieter Kasselman, and myself profiles the Shared Signals Framework for workload identity lifecycle events. SSF, CAEP, and RISC solved the human identity signal story, and WISE extends the same asynchronous event model to workloads: credential rotation, trust revocation, posture changes, and the security state transitions that arise when agents act on behalf of humans, on behalf of other agents, or autonomously. It builds on RFC 8417 (SET), the WIMSE identifier and credential specifications, and the CAEP Interoperability Profile patterns.

All of these are exactly the kind of focused, incremental work that the AIMS framework was designed to surface as missing.

What this means for practitioners

If you’re an identity architect or an IAM practitioner reading this, here’s the takeaway: you already have most of what you need to secure agentic workloads. The standards are written. The protocols are mature. What’s been missing is the map showing how they connect as well as the clarity on where the gaps actually are.

That’s what AIMS provides. And where the map shows blank spots, the community is filling them in with targeted, interoperable specifications rather than competing greenfield stacks.

We’re heading into IETF 126 in Vienna later this month. The AIMS draft is already shaping the standards landscape. I expect to have more exciting updates as those meetings wrap up. In the meantime, the pieces are on the table. Time to assemble.

About the Author

Jeff Lombardo is a Principal Identity Specialist at AWS based in Montreal. He contributes to IETF and OpenID Foundation work on identity architecture for agentic systems. The views expressed here are his own.

The post Assembly Required: How we Stopped Reinventing and Started Composing Standards for Agentic Identity appeared first on IDPro.

Part 2 of 3

By Vidyaa Ganesh

This is Part 2 of a three-part series on IAM maturity measurement. Part 1 reviewed the published research showing that 60-70% of organizations remain at early-to-mid stages of IAM maturity. This installment examines the frameworks currently available and the structural reasons no standard has emerged.

If 60% to 70% of organizations are stuck at early-to-mid stages of IAM maturity, as five independent research sources consistently show, a natural question follows: why can’t they measure and track their way out? The answer is not that frameworks are absent. Several exist. The answer is that they are incompatible with each other and, in most cases, not designed for cross-organizational comparison.

CMMI and General-Purpose Maturity Models

The Capability Maturity Model Integration, now maintained by ISACA, provides a well-established framework for assessing process maturity across domains. Its five-level structure (Initial, Managed, Defined, Quantitatively Managed, Optimizing) has been widely adopted outside its original software engineering context. CMMI’s staged representation introduces an important concept: lower-level capabilities must be satisfied before higher levels can be claimed. An organization cannot skip foundational process areas and still achieve a high maturity rating.

CMMI’s limitation for IAM is that it is domain-agnostic. It provides structure and principles, but it does not define what IAM-specific capabilities should be measured, how they should be weighted, or what constitutes a reasonable benchmark for a given industry.

Gartner IAM Program Maturity Model

Gartner’s IAM Program Maturity Model, published in September 2025, defines six dimensions of IAM maturity across five levels. It is perhaps the most authoritative vendor-neutral reference available, and its dimension structure (covering governance, identity lifecycle, access management, privileged access, and related areas) reflects a comprehensive view of what IAM programs should include.

However, the model is paywalled, which limits its utility as a shared community standard. It also does not publish empirical benchmark data showing where organizations in specific industries typically fall on its scale. Without that benchmark layer, an organization can assess itself against the model’s definitions but has no way to know how it compares to its peers.

SailPoint Horizons Framework

SailPoint’s Horizons framework is notable because it publishes actual empirical data. The five-horizon model is based on annual surveys, uses a clustering algorithm to assign organizations to maturity levels, and breaks results out by industry, geography, and organizational size. It also explicitly incorporates the concept of capability prerequisites: to be placed in a given horizon, an organization’s capabilities must cover most environments and identity types.

The limitation is that SailPoint is a vendor with commercial interests in the identity governance space. While the research methodology appears sound, a vendor-published framework will always face questions about objectivity, particularly when the recommended path to higher maturity runs through capabilities that the vendor sells.

CISA Zero Trust Maturity Model

The Cybersecurity and Infrastructure Security Agency published a Zero Trust Maturity Model that includes an identity pillar with explicit maturity levels. It is publicly available and government-backed, which gives it credibility. The model explicitly states dependencies between pillars: identity capabilities must be established before device trust or network trust can be meaningful.

The model is scoped to zero trust architecture, not IAM broadly. It does not cover domains like identity governance and administration, customer identity, or the operational and organizational dimensions of an IAM program. It is useful as a reference but incomplete as a general-purpose IAM maturity standard.

Vendor-Specific Models

Several vendors have published maturity models specific to their market segment. Okta published a four-stage CIAM maturity curve (Basic, Automated, Intelligent, Continuous). Auth0, now part of Okta, published an Identity Maturity Framework with six assessment dimensions. WSO2 published a five-level CIAM maturity model.

These models are useful for understanding capability progression within a specific domain, but they share a common limitation: none publishes empirical data about where organizations actually fall on their respective scales. They define the levels but do not populate them with benchmark data.

The Comparability Problem

The fundamental issue is not that frameworks are absent. It is that they are mutually incompatible. An organization assessed using SailPoint’s five-horizon model cannot compare its results to one assessed using Bravura’s four-level model or Gartner’s six-dimension framework. The scales differ, the dimensions differ, the weighting logic (where it exists) differs, and the definitions of what constitutes each level differ.

For IAM practitioners, this means that changing consultants often means starting the measurement process from scratch. For CISOs reporting to boards, it means that year-over-year comparisons are only valid if the same assessment approach is used each time. For the industry as a whole, it means there is no aggregate data pool that could raise the bar for everyone.

Table 2. Comparison of existing IAM maturity frameworks

Why No Standard Has Emerged

Given the clear need for standardized measurement, it is reasonable to ask why one does not already exist. Several structural factors have worked against the emergence of a shared standard.

Vendor incentives cut against standardization. Identity vendors benefit from publishing their own maturity models because it frames the conversation in terms of their product capabilities. A vendor’s maturity model will, almost by definition, position the vendor’s strongest features as markers of advanced maturity. This creates a structural incentive against converging on a shared, vendor-neutral standard.

IAM spans too many domains. IAM encompasses identity governance and administration, privileged access management, workforce authentication, customer identity, cloud identity, identity threat detection, and governance and strategy. Each has its own maturity curve, vendor landscape, and regulatory drivers. Building a single model that meaningfully covers all of them requires significant domain expertise and difficult weighting decisions.

No governing body has taken ownership. Unlike financial accounting (which has GAAP and IFRS) or software process maturity (which has CMMI), identity management does not have a single governing body that has taken responsibility for defining and maintaining a measurement standard. Organizations like IDPro, IDSA, NIST, and ISACA each contribute pieces of the puzzle, but none has published a comprehensive, empirically-grounded IAM maturity standard.

Measurement requires difficult methodological choices. How should domains be weighted against each other? Should privileged access management carry more weight than governance? How do you handle the scenario where an organization scores highly in advanced areas but has gaps in foundational controls? These are not trivial questions, and without empirical data to validate different approaches, any methodology choice can be challenged.

The CIAM measurement gap. While workforce IAM has at least some benchmark data available, the CIAM space has essentially none. Gartner’s 2025 research found that over 50% of organizations still use homegrown or no CIAM solution at all. Multiple vendors have published CIAM maturity models, but none has published empirical data about where organizations actually fall on those models.

Endnotes

9. ISACA, CMMI Version 3.0 (CMMI Institute/ISACA, 2023).

10. Gartner, Inc., Identity and Access Management Program Maturity Model (September 2025), Document ID: 1203314.

11. SailPoint, Horizons 2025-2026, Appendix, p. 44.

12. Cybersecurity and Infrastructure Security Agency, Zero Trust Maturity Model (CISA, 2023).

13. Okta, Inc., From Zero to Hero: The Path to CIAM Maturity (Okta eBook).

14. Auth0/Okta, Auth0 Identity Maturity Framework (IMF) (Auth0, 2021).

15. WSO2, A Maturity Model for Customer IAM (WSO2 Blog).

16. Gartner, Inc., Innovation Insight for Customer and Partner IAM (April 2025).

NEXT IN THIS SERIES

Part 3: What Good Measurement Looks Like

If the IAM community is going to move toward standardized measurement, what would a credible framework need to include? Seven design principles, the open questions that remain, and a call to action.

About the Author

Vidyaa Ganesh is a Senior IAM Engineer and a solutions architect with over six years of experience delivering identity governance programs for financial services, energy, telecommunications, and public sector clients. She holds a Master of Engineering from Concordia University, is a member of IDPro, and is the creator of AXIS (axis.identara.ca), an open IAM maturity assessment framework.

The post Assembly Required: How We Stopped Reinventing and Started Composing Standards for Agentic Identity appeared first on IDPro.