User Access Review AI Analyst - Cyber Sierra
User Access Review AI Analyst
Your last access review took 20 FTE days across 12 countries. This one takes days.
The User Access Review AI Analyst ingests HR records, Active Directory, RBAC definitions, and application entitlements in whatever format they exist today: no system restructuring required. It reconciles every source into a unified view, flags anomalies, routes manager certifications, and produces auditor-ready documentation.
20+ FTE days → days
per semi-annual review cycle, live customer data
2,087 employees
reviewed across 6 applications, one AI run
12 countries
country-specific RBAC preserved, no restructuring
The Problem
Your UAR process was built for 200 employees. You have 2,000 across 12 countries.
01
Every cycle starts with two days of data wrangling before any review begins.
One team member at a multinational bank spent two full days every month just reconciling HR records against Active Directory. Not reviewing access. Not making decisions. Just joining spreadsheets. Multiply that across 12 countries with different HR systems and RBAC models, and you burn weeks before a single manager sees a certification.
02
2,087 employees. Six applications. One spreadsheet that breaks every cycle.
Credit Saison India's lean security team managed user access reviews for over 2,000 employees across half a dozen apps on Excel. Fragile formulas, manual judgment calls, and no unified view. One corrupted cell and the entire certification cycle was compromised.
03
Manager approvals are scattered across untraceable email threads.
A manager clicks 'Approved' in an email: no timestamp, no audit trail, no way to prove to a regulator that the review actually happened. When the RBI or MAS auditor asks for certification records, you're forwarding email screenshots.
04
Terminated employees. Role mismatches. Access outside RBAC. You're finding these by hand.
Security teams know the patterns: ex-employees still active in systems, permissions that don't match job roles, access granted outside approved RBAC definitions. But finding them means manually comparing HR exports against Active Directory against application entitlement files: line by line, country by country.
How the User Access Review AI Analyst Works
Connect your identity sources in their existing formats. The AI reconciles everything into a unified entitlement view, flags policy violations and anomalies, and routes a structured certification workflow to every manager: with timestamped decisions and downloadable audit artifacts.
01
Connect
Point the AI at your HR system extracts, Active Directory, RBAC definitions, ticketing records, and application entitlement files: in whatever format they exist today. No standardization. No preprocessing. No system restructuring.
02
Reconcile
The AI joins every source into a single unified entitlement view. It flags terminated employees still active, role-to-access mismatches, permissions outside RBAC definitions, and exceptions requiring escalation: not raw data dumps, but structured, actionable findings.
03
Certify
Structured review assignments go to every manager. They approve or revoke access inside the platform: every decision timestamped, every action logged. Remediation outcomes route automatically to IAM and application teams. Downloadable certification documentation ready for your auditor.
app.cybersierra.co / uar-analystMulti-source identity ingestion
HR Extract, iChris
- 2,087 employees Ingested
- Active Directory: 3,140 accounts Ingested
- RBAC Definitions: 12 countries Ingested
- App Entitlements: 6 applications Ingested
Capabilities
Reads Your Data as It Sits. Doesn't Ask You to Rebuild Your IAM.
Most access governance tools demand standardized identity feeds before they'll work. The User Access Review AI Analyst ingests what you already have: HR extracts in any format, Active Directory as-is, country-specific RBAC structures preserved: and produces a unified, auditable certification record.
Any-Format Ingestion
Pull identity data from iChris, Workday, SAP SuccessFactors, or a custom CSV export. Active Directory dumps. Application entitlement files. No upstream data restructuring. No forced standardization. The AI reads what you have, the way you have it.
Multi-Source Reconciliation
The AI joins HR records against Active Directory, RBAC definitions against actual entitlements, and ticketing data against access levels. For a bank operating across 12 countries, that meant automated reconciliation that previously cost one team member 2 full days every month.
Pre-Built Exception Flagging
Terminated employees still active. Permissions that don't match approved roles. Access outside RBAC boundaries. The AI doesn't dump data: it flags specific, policy-aligned exceptions with the source attribution and recommended action attached.
Manager Certification Workflow
Every manager gets a structured certification assignment with their team's entitlements, the flagged exceptions, and approve/revoke controls. No email threads. No spreadsheet attachments. Every decision timestamped and logged for the auditor.
Auditor-Ready Documentation
When the regulator asks for evidence of your access review, you download a complete certification package: manager decisions, timestamps, exception handling, remediation routing. The same bank that previously spent 20+ FTE days per cycle now gets positive auditor feedback on documentation quality.
Country-Specific RBAC Preserved
The same application can have different RBAC structures in different countries. The AI preserves those local governance models while creating enterprise-wide visibility. No forced standardization. No 'one-size-fits-all' role model that doesn't fit your operations.
Compare
What Fails When You Scale Access Reviews Past 500 Employees
Every organization running periodic access reviews has tried to make it work with the tools they already have. Here's where each approach collapses under enterprise scale, and what the User Access Review AI Analyst does instead.
| Approach | What Breaks | What the AI Analyst Does |
|---|---|---|
| Excel + Email | Fragile formulas and manual judgment across multiple spreadsheets. HR data in one file, Active Directory in another, application entitlements in a third: joined by hand every cycle. Manager approvals scattered across inboxes with no audit trail. The entire process depends on one analyst not making a formula error. | Every source ingested and reconciled automatically. Country-specific RBAC models preserved while anomalies are surfaced systematically. Manager certifications are timestamped, traceable, and auditor-ready. 2,087 employees across 6 apps: from 1 month to under 1 week. |
| IGA / IAM Platforms | An identity governance tool manages the lifecycle but demands standardized feeds and structured role models before it can operate. If your 12 countries have 12 different RBAC structures, the implementation project takes 12-18 months and costs more than the tool itself. | No upstream data restructuring. No forced role standardization. The AI reads identity data in its existing format: HR extracts, AD, RBAC definitions, app entitlement files: and produces a unified, auditable certification record in days. |
| Manual Sampling | At enterprise scale, reviewing every user is impossible: so teams sample. Spot-check 200 out of 2,000 employees and hope the pattern holds. It doesn't. A single missed terminated employee with active system access is a regulatory finding waiting to happen. | 100% coverage. Every user, every entitlement, every system: reviewed by AI. Human reviewers certify the flagged exceptions, not the full population. 0% false negatives across live deployments. |
| Annual Audit-Only Reviews | Access reviews triggered only by audit deadlines leave months of unexamined access changes between cycles. A user changes roles in March, retains old permissions until the December review, and your auditor finds it before you do. | The AI runs at whatever cadence your policy requires: monthly, quarterly, semi-annual. Each cycle completes in days, not weeks. Ad-hoc reviews triggered by M&A, reorganization, or a regulator's request are no longer a panic event. |
Proven Impact
What Changed When Enterprises Switched From Spreadsheets to AI
Observed outcomes from production user access review deployments across banking, financial services, and professional services.
20+ FTE days → days per semi-annual review cycle
A multinational bank operating across 12 countries reduced its semi-annual access review from over 20 FTE days to a process that completes in days. Country-specific RBAC models across different applications were preserved: the AI ingested local role structures as-is, with no forced standardization and no system restructuring.2 full days/month baseline reconciliation time eliminated
One team member at this bank previously spent two full days every month reconciling HR records against Active Directory: not reviewing access, just joining data. The AI now performs this reconciliation automatically every cycle, freeing that analyst for actual security work.2,087 employees across 6 applications in one review cycle
Credit Saison India completed a full user access review for 2,087 employees spanning 6 applications. What previously stretched over a month of manual spreadsheet work now completes in under a week. The same lean security team, zero additional headcount.700+ employees bulk policy acknowledgment with full audit trail
A professional services firm with 700+ employees used the platform to push policy acknowledgments via Azure SSO: every employee, every acknowledgment timestamped and logged. The firm achieved ISO 27001 certification under tight resource constraints and a compressed timeline.
“We received positive feedback from our regulatory auditors on the quality of our access certification documentation.” – IT Governance Lead, multinational bank (12 countries)
FAQ
Do we need to restructure our identity data or standardize RBAC before the AI can work?
No. This is the single most common blocker with traditional IGA tools, and we designed the User Access Review AI Analyst to bypass it entirely. It ingests HR system extracts (iChris, Workday, SAP, custom CSVs), Active Directory exports, RBAC definitions, and application entitlement files in whatever format they exist today.
Country-specific role models are preserved as-is. No upstream restructuring, no forced standardization, no 12-month implementation project.
We already have an IAM or IGA platform. Why do we need this?
Most IGA platforms manage identity lifecycles and provisioning. They are not purpose-built for the certification workflow: the periodic review where managers formally attest to their team's access.
The User Access Review AI Analyst handles the certification process specifically: multi-source reconciliation, exception flagging, manager routing, decision capture, and audit-ready documentation. It sits alongside your existing IAM investment.
How does it handle multi-country operations with different regulations?
RBAC structures and regulatory requirements are preserved per country. The platform supports configurable certification policies per geography, while providing enterprise-wide visibility.
A bank operating in 12 countries can run one review cycle with country-specific rules rather than 12 separate manual exercises.
Does our identity data leave our environment?
No. The User Access Review AI Analyst deploys in your own cloud, on-premises, or air-gapped with your choice of LLM. HR records, Active Directory data, and entitlement information never leave your environment.
IMDA accredited. Deployed on Singapore GCC.
Can we run a pilot on one country or one application first?
Yes. Most customers start with a scoped proof-of-value: one geography, one set of applications, one review cycle. You see the AI reconcile your actual identity data, flag real anomalies, and produce a certification package.
The pilot is typically 3 to 6 months, and the cost is credited toward your annual contract on conversion.