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12/06/2026 10:00 pm
TL;DR: FAIR gives security teams a way to translate identity risk into expected financial loss, using Loss Event Frequency and Loss Magnitude to turn scattered IAM, SaaS, and cloud signals into executive-ready estimates, according to Axiad. That matters because siloed identity tools undercount exposure when orphaned access, OAuth sprawl, and exposed credentials sit outside a single control view.
NHIMG editorial — based on content published by Axiad: FAIR: How to Quantify Your Identity Risk in Business Terms
By the numbers:
- The 2024 global average sits at $4.88 million per incident, with credential-based breaches and those involving identity system compromise trending toward the higher end of the distribution.
Questions worth separating out
Q: How should security teams quantify identity risk in financial terms?
A: Start by modelling identity risk as loss event frequency and loss magnitude, then feed the model with real exposure data from IAM, SaaS, and cloud systems.
Q: Why do siloed IAM tools make identity risk harder to measure?
A: Because each tool sees only one slice of the access environment.
Q: What do security teams get wrong about orphaned access and risk scoring?
A: They often treat orphaned access as a hygiene issue instead of a loss driver.
Practitioner guidance
- Map reachable identity exposure across all control planes Correlate IGA, SSPM, CIEM, and IdP data so you can see accounts, grants, tokens, and trust relationships in one view.
- Quantify dormant access as a financial risk, not a cleanup queue Score orphaned accounts, temporary grants, and inactive OAuth connections by the systems and data they can still reach.
- Use exposed credentials as a trigger for lifecycle review When a credential appears in breach datasets or dark web monitoring, re-evaluate the full identity path behind it, including MFA posture, privilege scope, and whether the grant still has business justification.
What's in the full article
Axiad's full blog post covers the operational detail this post intentionally leaves for the source:
- A step-by-step explanation of how the FAIR model breaks identity risk into frequency and magnitude inputs.
- Examples of how Axiad Mesh correlates identity signals across SaaS, cloud, and federated access paths.
- A worked business-risk narrative for orphaned accounts, OAuth sprawl, and exposed credentials.
- The article's embedded FAIR framing and report offer if you need to build an executive business case.
👉 Read Axiad's analysis of FAIR for identity risk quantification →
Identity risk quantification with FAIR: what IAM teams need now?
Explore further
13/06/2026 12:29 am
FAIR only becomes useful for identity when visibility is already a governance capability. The model is not a substitute for knowing what exists, who can reach it, and which grants remain live after business context has expired. If the identity picture is partial, the financial estimate is partial too. Practitioners should treat identity visibility as a prerequisite to credible risk quantification.
A few things that frame the scale:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, and a quarter have faced multiple attacks.
A question worth separating out:
Q: How do security teams decide which identity fixes to fund first?
A: Use expected loss reduction per dollar spent, not the length of the backlog. Remediation should be ranked by how much exposed access it removes, how sensitive the reachable systems are, and how likely the identity path is to be abused. That approach makes identity investment defendable to finance and more consistent across IAM, NHI, and SaaS risk.
👉 Read our full editorial: FAIR for identity risk turns backlog into business loss estimates
