Identity visibility and intelligence is wider than ISPM alone

At a glance

What this is: This is an analysis of why identity security posture management is not the whole problem, and why unified identity visibility and intelligence is emerging as the broader governance layer.

Why it matters: IAM teams need a way to correlate risk across human and non-human identities because siloed tools can miss blast radius, over-privilege, and cross-system attack paths.

By the numbers:

• Gartner projects that by 2028, 70% of CISOs will be using an IVIP to shrink their IAM attack surface.
• Only 5.7% of organisations have full visibility into their service accounts.
• 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
• 96% of organisations store secrets outside of secrets managers in vulnerable locations including code, config files, and CI/CD tools.

👉 Read Axiad's analysis of IVIP and identity attack surface reduction

Context

Identity security posture management is the practice of finding identity hygiene gaps such as over-privileged accounts, weak authentication, dormant access, and misconfigured policies. The problem is that most enterprises now run several identity tools in parallel, so the evidence is split across directories, PAM, IGA, ITDR, SaaS platforms, and machine identity systems.

That fragmentation matters because practitioners do not just need isolated findings. They need a unified view of identity risk across human and non-human identities, including the relationships that determine blast radius, compromise paths, and whether a finding is actually actionable within the broader IAM programme.

Key questions

Q: How should security teams unify identity risk across IAM tools?

A: Security teams should correlate identity data from directories, PAM, IGA, ISPM, SaaS, and machine identity systems into one risk view. That lets them see which findings overlap, which identities have broad blast radius, and which controls are failing together. Without correlation, teams only get local findings, not an enterprise identity risk picture.

Q: Why do machine identities complicate identity posture management?

A: Machine identities complicate posture management because they are numerous, persistent, and often poorly owned. Service accounts, keys, tokens, and certificates can outlive the systems that created them, which makes it hard to know whether access is still needed. That ambiguity increases blast radius and weakens revocation decisions.

Q: How do you know if identity posture tooling is actually working?

A: Identity posture tooling is working when it reduces time to answer critical questions, closes the gap between finding and remediation, and shows a measurable decline in over-privilege and dormant access. If it only creates more alerts without improving decision speed or closure rates, it is not delivering governance value.

Q: What is the difference between ISPM and identity visibility platforms?

A: ISPM focuses on posture findings such as misconfigurations, excessive permissions, and weak authentication within a narrower slice of the environment. An identity visibility platform correlates those findings across the full identity stack and turns them into a unified intelligence model. The difference is scope, correlation, and decision quality.

Technical breakdown

How identity visibility and intelligence platforms differ from ISPM

ISPM focuses on posture findings within a narrower slice of identity infrastructure, such as excessive permissions, policy drift, and weak authentication settings. An identity visibility and intelligence platform sits above those point solutions and correlates the data they already produce. The technical difference is aggregation plus relationship mapping. That means connecting identity events, configuration state, and entitlement data across multiple sources so security teams can see patterns that no single tool can prove on its own. In practice, the value is not visibility as a dashboard, but visibility as a way to turn fragmented identity evidence into a single risk narrative.

Practical implication: map which identity controls still operate in silos before you decide whether posture tooling is enough.

Why machine identity visibility changes the risk model

Machine identities such as service accounts, API keys, OAuth tokens, certificates, cloud roles, and AI agents create a risk profile that differs from human accounts because they are abundant, persistent, and often lightly governed. Their permissions are frequently inherited across systems, and their blast radius can extend far beyond the system where they were created. When these identities are not inventoried or correlated with usage, teams cannot tell whether a credential is dormant, over-scoped, or quietly central to production processes. The technical issue is lifecycle opacity: the identity exists, but its purpose, dependencies, and revocation path are unclear.

Practical implication: build machine-identity inventory and relationship data into your posture model, not just human account reviews.

Why quantified identity risk matters to the board

Quantified identity risk turns posture findings into a decision model. Annualized Loss Expectancy, or ALE, is one way to express potential cost in financial terms rather than only as a technical score. That matters because identity programmes usually compete for budget against controls that can show incident counts, not just hygiene defects. When identity risk is correlated across systems and translated into financial exposure, teams can compare remediation options based on likely loss reduction. The technical point is that risk correlation becomes operational only when scoring, prevalence, and attack-path context are combined in one place.

Practical implication: translate identity findings into loss exposure so remediation can be prioritised using the same language as the board.

Breaches seen in the wild

• BeyondTrust API key breach — compromised BeyondTrust API key led to unauthorized SaaS access.
• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

Identity visibility and intelligence is the missing control plane, not another point product. ISPM is useful, but it only addresses a subset of the identity problem by surfacing hygiene defects in isolated systems. The broader issue is that enterprises have accumulated identity tools without a unifying intelligence layer, so risk remains fragmented across directories, SaaS, PAM, and machine identity systems. The implication is that mature IAM programmes now need correlation as a control objective, not just more findings.

Machine identity sprawl changes the meaning of identity governance. Service accounts, API keys, tokens, certificates, cloud roles, and AI agents outnumber human identities in many environments, which means the dominant identity risk surface is no longer human-centric. That shift exposes the limits of posture tools designed mainly around user access. Practitioners should treat machine identity inventory, lineage, and blast radius as first-class governance data, because the greatest exposure often sits outside the accounts that traditional IAM teams review first.

Quantification is becoming a governance requirement, not a reporting feature. Security leaders cannot defend remediation priorities if identity findings remain disconnected from business impact. ALE-style scoring matters because it lets identity risk compete in the same language as operational and financial decisions. The practical conclusion is that programmes that cannot translate posture into exposure will struggle to justify closure of the highest-risk identity gaps.

Identity attack surface management is expanding from access control into intelligence correlation. The category boundary is moving because identity programmes now need to understand relationships, not just entitlements. That includes which identities are linked to which systems, which credentials are still active, and which findings are mutually reinforcing. The practitioner takeaway is to re-evaluate whether existing tools provide a single decision view or merely generate more disconnected tickets.

Named concept: identity intelligence layer. This is the connective function that turns siloed IAM, PAM, ISPM, and machine identity data into a usable governance picture. It matters because the programme failure is rarely a lack of security tools. It is the absence of a layer that can reconcile them into one prioritised risk model. Practitioners should treat that layer as an architectural requirement for modern identity governance.

From our research:

• 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
• 71% of NHIs are not rotated within recommended time frames, increasing the risk of compromise over time.
• For the broader lifecycle context, see 52 NHI Breaches Analysis for recurring failure patterns and control gaps.

What this signals

Identity intelligence will become the practical layer that decides which identity findings matter first. As environments accumulate more tools, teams need a way to correlate posture, entitlement, and lifecycle data into one operating picture. A programme that cannot unify those signals will keep producing tickets instead of decisions, especially once machine identities dominate more of the access surface.

Identity blast radius is the concept to watch. Once a team can trace how a single identity connects to multiple systems, it can separate local hygiene from enterprise exposure. That shift is central to NHI governance, because a weak account is not always the main problem. The real issue is often how far that account can move before detection or containment.

With 96% of organisations storing secrets outside secrets managers in vulnerable locations including code, config files, and CI/CD tools, the governance gap is already structural. Teams should expect posture programmes to expand beyond review cycles and into correlation across credential stores, lifecycle controls, and access paths.

For practitioners

• Inventory identity data sources across the stack List every system that holds entitlement, authentication, or lifecycle data, including directories, PAM, IGA, SaaS, and machine identity stores. The goal is to identify where posture findings are stranded and where correlation is currently impossible.
• Add machine identities to governance reviews Include service accounts, API keys, OAuth tokens, certificates, and cloud roles in recurring identity reviews so non-human access is assessed alongside workforce access. Cross-check ownership, purpose, and dependency chains before approving continued use.
• Quantify identity risk in business terms Translate the most material identity findings into loss exposure, blast radius, or estimated remediation value so leaders can compare identity risk with other security priorities. Use the same financial framing across human and non-human identity programmes.
• Tie posture findings to remediation ownership Map each identity finding to the system that can actually fix it, then confirm that the workflow exists across identity providers, authentication controls, and lifecycle tools. Findings without a path to closure become recurring noise.

Key takeaways

• Identity security posture management is useful, but it does not solve the larger problem of fragmented identity intelligence.
• Machine identities now represent a major share of the identity attack surface, which makes correlation and lifecycle visibility essential.
• Practitioners need to translate posture findings into business risk if they want identity governance to influence remediation priority.

Key terms

• Identity Security Posture Management: Identity Security Posture Management is the practice of continuously finding and prioritising identity hygiene gaps across an environment. It looks for excessive permissions, weak authentication, dormant access, and policy drift, but it usually works best when paired with broader correlation across identity systems.
• Identity Visibility and Intelligence Platform: An Identity Visibility and Intelligence Platform is a layer that correlates identity data across multiple tools into one risk picture. It does not replace existing controls. It makes them more useful by connecting events, relationships, configuration, and posture so teams can prioritise what matters.
• Machine Identity: A machine identity is any non-human identity used by a workload, service, integration, or automated process. Examples include service accounts, API keys, OAuth tokens, certificates, and cloud roles. These identities often persist longer than expected and need lifecycle governance, not just storage.
• Annualized Loss Expectancy: Annualized Loss Expectancy is a financial estimate of the expected cost of a risk over a year. In identity governance, it helps translate technical findings into business language so teams can compare remediation options and explain why one identity issue deserves priority over another.

Deepen your knowledge

Identity visibility, machine identity governance, and quantified risk are covered in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building a unified identity governance model across human and non-human identities, it is worth exploring.

This post draws on content published by Axiad: A CISO Called Us an ISPM Vendor. Here's What We Told Him. Read the original.