Architecting IAM Systems That Secure 100 Million User Credentials

Managing identity and access for millions of users requires expertise in security architecture, regulatory compliance, and performance optimization. With 18 years of experience in Identity and Access Management, Anoop Gopi has architected solutions handling over 100 million customer credentials and billions of daily authorizations in the financial services sector.

In this interview, Anoop shares his approach to building IAM systems that balance security with usability, implementing NIST-compliant frameworks, and executing zero-downtime migrations of critical authentication systems. He discusses the role of automation in modern IAM operations, how AI is transforming fraud detection and risk assessment, and provides practical advice for organizations planning large-scale IAM modernization projects.

What are the most critical considerations when architecting IAM solutions that need to handle 100+ million user credentials?

First, security is non-negotiable; never store credentials in plain text. Always hash credentials using modern secure hashing algorithms like Argon2 or Bcrypt, including a unique per-user salt. All the credential transport and storage must be secured using TLS and enforcing encryption at rest and in transit, including for active data and any backups.

A strong key management is also essential; keys used for encryption/decryption need to be securely stored in a managed KMS (or HSM), and with regular key rotation enabled with an ability to rotate keys on demand. Follow a strictly least privileged principle, using RBAC for any administrative operation. Ensure environment separation with limited access to credential stores, utilizing MFA (multi-factor authentication) enabled by default. The system also needs to employ consistent monitoring and alerting for any access to credential stores.

Conduct a half-yearly or yearly threat model exercise to audit security controls and keep incident response playbooks updated.

Second, privacy and compliance. Strictly follow the compliance requirements defined by privacy laws, PCI-DSS, SOC2, and ISO27001, and based on any local privacy rules. Store only required attributes and define the data retention and deletion policies (based on GDPR, CCPA/).Have the ability to track user consent, opt-outs, and any account activity when required, implementing audit trails for all operations on the user data.

Third, design and build for scale and performance. Design the end-to-end system with scale and performance in mind, employing stateless, horizontally scalable front-end components and a microservice architecture for backend services, complemented with a scalable, sharded, or partitioned credential store. Distribute user data across the store using hashed keys to spread the read and write loads evenly; design to limit upstream datastore connections to minimize any coordination during any database patching or upgrades.

Where possible, leverage horizontally scalable services to balance read and write throughput according to demand and implement caching for non-sensitive user data with strict cache invalidation procedures. Designing system architecture for non-critical workloads like analytical use case, checking for any breach data, etc. to be asynchronous, keeping the critical authentication flow synchronous with low latency

Regularly perform capacity planning and stress testing to confirm the system’s ability to handle peak login and any burst behavior. Employ auto-scaling features to adapt to changing demand. Tune the application using chaos engineering and incorporating retry and backoff logic to prevent any cascading failures during unexpected disruptions.

Implement high availability and fault tolerance by deploying applications across multiple availability zones and regions, with the ability to failover in the event of any AZ or region failure scenario. Exercise quarterly Traffic and Data Recovery Exercises to validate the system’s ability to recover during an outage and if it meets the RTO/RPO needs.

Q: How do you approach implementing NIST-compliant security frameworks while maintaining user experience and system performance?

Implementing a NIST-compliant security framework, such as SP 800-53 or CSF 2.0, requires a structured, risk-based approach that integrates security controls without unduly burdening users or degrading system performance. It is critical to ensure proactive measures that can enhance protection while seamless interactions and operation speed are maintained:

• Design identity and access management systems with security and user experience. Implement NIST SP 800-63 guidelines for digital identity, authentication, and federation early in the design stage, mapping controls directly to user journeys such as onboarding, MFA enrollment, and password recovery.
• Adopt a user-centric, low-friction approach that utilizes adaptive authentication, adjusting security requirements based on user behavior, device, context, or risk level, rather than applying uniform security measures. Transition toward passwordless or biometric login methods (e.g., facial recognition or fingerprints) to reduce friction while maintaining compliance and security. Implement phishing-resistant MFA that operates seamlessly for low-risk actions but steps up verification for sensitive operations such as fund transfers or privileged access.
• Apply Zero Trust and least-privilege principles to minimize attack surface and unnecessary access. Employ tiered, risk-based controls aligned with NIST Identity Assurance (IAL) and Authentication Assurance Levels (AAL) for example, AAL1 for known devices, AAL2 with step-up MFA for new logins, and AAL3 for high-risk transactions.
• Use secure, token-based authentication (JWT, OAuth, OIDC) to enable single sign-on (SSO), seamless navigation, and session continuity with silent re-authentication and minimal latency. Implement cryptographic best practices for hashing and encrypting data in transit and at rest. Finally, manage evolving security requirements through policy-driven configurations using tools like OPA, Drools, or Camunda, enabling quick updates to security controls without disrupting user experience.
• Leverage NIST’s “Detect, Respond, and Recover” technique to evaluate and refine business implementation. For example, implementcontinuous control monitoring to validate the NIST controls are in effect and active; Observe for user behaviors like drop off during MFA setup or password resets, and feed back into the UX design, maintaining key control compliance.
• Implement quarterly compliance training with interactive modules to build awareness and foster an environment where compliance becomes part of development and design.
• Monitor system metrics to detect any performance dips, access control rules and build an iterative process to ensure there is a clear balance to ensure the system adapts to the evolving threat landscape, sustaining optimal UX and efficiency.
• Balance NIST’s emphasis on controls like encryption, audit logging, and monitoring with efficiency to avoid latency and resource constraints.
• Utilize cloud-native infrastructure and content delivery networks to offload security tasks and reduce server load, while ensuring compliance with standards such as SOC 2.
• Automate permission and risk analysis, leveraging AI-driven modes that can adjust based on user and system risk profiles.
• Executing regular performance testing to ensure layered security controls are in place, including edge layer defenses, firewall restrictions, rate limiting, and multi-factor authentication controls to prevent unauthorized access.

Q: What strategies have you found most effective for zero-downtime migrations when dealing with critical authentication systems?

Migrating critical authentication systems, where even brief outages can cause widespread access issues, security risks, or user frustration, requires meticulous planning and techniques that ensure uninterrupted service. Based on established best practices, effective strategies focus on gradual transitions, redundancy, and robust validation for seamless operation. Below are some of the key approaches based on my experience

Begin with a thorough evaluation to minimize surprises. Assess data schemas, dependencies (e.g., credential store integrations with apps or APIs), and potential failure points. Develop detailed rollback plans and backup strategies, including point-in-time database recovery. This foundational step aligns migrations with zero-downtime goals by upfront identifying high-risk elements, such as credential synchronization and rollback strategies.

Design the credential data store interaction service with schemas that avoid breaking changes. Maintain versioned APIs (e.g., /v1, /v2) to isolate changes and centralize data store access through a unified service. This enables controlled migration via a central API, ensuring uninterrupted access for existing integrations.

Utilize backup and restore techniques for bulk user migration, in conjunction with real-time replication or change data capture (CDC), to synchronize old and new credential stores. Implement dual-write strategies on APIs with automated reconciliation to maintain consistency. This ensures data integrity throughout the migration process.

Employ blue-green deployments to run old (blue) and new (green) systems concurrently, using API gateways to route traffic. Start by directing 1-5% of traffic to the new system while monitoring metrics such as login success rates, latency, and error types. Alternatively, use shadow traffic to mirror production requests to the new system without impacting users, thereby validating behavior. Gradually increase traffic to the new system, setting the old system to read-only during final cutover for easy rollback. This ensures authentication performance parity and supports immediate failover with active-active multi-region setups.

Automate testing for data integrity (e.g. running period user record checks) and functionality (e.g. simulated logins or executing pilot user migration) throughout the process. Monitor real-time metrics, including error rates, response times, and authentication failures. Post-migration, run both systems briefly for observation to catch discrepancies (e.g., mismatched encryption keys) early, maintaining trust in system security and performance.

Q: How do you balance security requirements with the need for seamless partner integrations and user onboarding?

Achieving a balance between security requirements, such as those outlined in NIST frameworks, and the need for seamless partner integrations and user onboarding involves embedding robust security measures while minimizing friction. This requires a strategic approach using risk-based authentication, standardized protocols, and efficient processes. Below are key strategies to maintain compliance with security standards while ensuring a seamless experience for users and partners.

Adopt secure, widely used protocols like OAuth 2.0, OpenID Connect (OIDC), or SAML to simplify partner integrations and user authentication while aligning with NIST SP 800-63-3 for identity federation and authentication. These protocols streamline development and access while incorporating security features like token expiration and secure key exchange.

• Provide clear documentation and SDKs for OAuth/OIDC, enabling single sign-on (SSO) or token-based access. Utilize JSON Web Tokens (JWTs) with a shared JWKS endpoint for secure and interoperable token validation, thereby minimizing integration complexity.
• Support SSO via OIDC or SAML, allowing users to log in with existing credentials (e.g., Google or enterprise IDPs). Offer passwordless options like biometrics or one-time passcodes (OTPs) to streamline authentication while meeting NIST’s digital identity standards. Apply NIST’s Authentication Assurance Levels (AAL): AAL1 for low-risk scenarios (e.g., known devices with passwordless logins like email magic links) and AAL2 with phishing-resistant MFA (e.g., FIDO2 authenticators) for higher-risk actions (e.g., new device logins), ensuring security without overwhelming users.

Design onboarding processes to reduce friction while incorporating NIST-compliant security controls, balancing ease of use with robust protection:

• Follow NIST’s Identity Assurance Levels (IAL). Use IAL1 with minimal proofing (e.g., email verification) for low-risk applications and IAL2 with user-friendly remote proofing (e.g., document scanning or knowledge-based checks) for sensitive systems. Optimize flows with user sentiment analysis or A/B testing to avoid confusion.
• Collect user data gradually (e.g., basic details first, followed by additional data later) to lower the initial barriers. Store PII securely with encryption, adhering to NIST SP 800-53 data protection controls.
• Offer self-service portals for partners to register, configure APIs, and manage credentials. Automate key issuance and rotation using tools like KMS or HSM, ensuring NIST cryptographic compliance while simplifying the setup process. Leverage SCIM for efficient user lifecycle management.

Apply Zero Trust Architecture, as outlined in NIST SP 800-207, to secure integrations and onboarding without compromising performance or usability.

• Use fine-grained access controls, granting partners API scopes specific to their needs (e.g., read-only for analytics) and short-lived tokens to reduce risk. For users, implement role-based access control (RBAC) during onboarding to restrict access until verification is complete.
• Employ background checks like device fingerprinting or behavioral biometrics to validate users and partners without extra steps, aligning with NIST’s continuous authentication principles while maintaining smooth interactions.

Utilize real-time monitoring and feedback to refine security and integration processes, ensuring compliance and seamless operation.

• Track metrics such as onboarding completion rates, API latency, and authentication failures to identify areas of friction.
• Collect user and partner input through surveys or analytics to improve onboarding flows or integration setups. For instance, simplify documentation or provide templates if partners report complex API configurations.
• Adjust security controls (e.g., MFA frequency) based on usage patterns and risk profiles to maintain compliance while minimizing overburdening on users or partners.

Q: What role does automation play in modern IAM operations, and how do you implement it effectively at scale?

Automation plays a key role in modern Identity and Access Management (IAM), enabling scalability, enhancing security, and streamlining operations in complex, often hybrid or cloud-based environments. As organizations manage increasing numbers of users, devices, and applications, automation minimizes manual errors, ensures consistent policy enforcement, and supports compliance with regulations like GDPR, HIPAA, etc.

To implement IAM automation effectively at scale, a structured approach is essential to ensure security, compliance, and operational efficiency. Below are some key strategies to consider:

1. Conduct an evaluation of the current IAM environment to identify manual tasks (e.g., provisioning, access requests) and high-risk areas like dormant accounts. Define clear goals, such as faster onboarding or achieving regulatory compliance, that are aligned with business needs. For scalability, implement role-based access control (RBAC) or attribute-based access control (ABAC) to manage permissions across thousands of users and applications dynamically. Integrate with HR systems or partner directories for real-time data synchronization, ensuring automation adapts to growth without performance issues.
2. Choose scalable Identity tools with AI-driven features like anomaly detection and adaptive multifactor authentication (MFA). Use standardized protocols (SCIM, SAML, OIDC) for seamless integration with other Identity Providers and SaaS systems. Incorporate AI for advanced capabilities, such as behavioral biometrics, predictive threat mitigation, and automated workflows (e.g., just-in-time access for privileged access management, or PAM). At scale, leverage policy engines (e.g., Open Policy Agent) to enforce Zero Trust principles, granting short-lived access and monitoring sessions in real-time
3. Automate PAM to secure high-risk access with minimal manual intervention. Implement just-in-time access, granting temporary permissions for sensitive tasks, and automatically revoke them after the task is completed. Use event-driven workflows to synchronize privilege changes across systems, ensuring compliance and reducing risk exposure. For example, integrating HR systems with PAM ensures that access is automatically adjusted as roles change, minimizing vulnerabilities such as orphaned privileged accounts.
4. Automate access reviews to maintain compliance and security:
   1. Trigger quarterly certification campaigns automatically.
   2. Use pre-filtered recommendations to remove unused access or flag anomalies.
   3. Enable bulk certification for standard access and leverage machine learning to highlight high-risk access for manual review.
   4. Automatically revoke uncertified access after deadlines to ensure consistent policy enforcement.
5. Define IAM policies declaratively using frameworks like Open Policy Agent (OPA) for version-controlled, testable configurations. Implement event-driven pipelines to synchronize identity changes across distributed systems in near real-time, such as role updates or deprovisioning triggered by HR events. Standardize APIs with SCIM and OIDC for seamless interoperability across cloud and partner platforms, ensuring secure and scalable integrations.

Q: How do you see the intersection of AI and IAM evolving, particularly in areas like fraud detection and risk assessment?

The intersection of AI and IAM is rapidly transforming, driven by the need to address escalating cyber threats, manage complex digital ecosystems, and enhance user experience. AI is becoming integral to IAM, particularly in fraud detection and risk assessment. AI’s ability to analyze vast datasets, detect patterns, and adapt in real-time enables organizations to mitigate risks while maintaining seamless access proactively. Outlined below are how this intersection is evolving and key strategies for leveraging AI effectively in these areas:

1. AI-powered User and Entity Behavior Analytics is revolutionizing fraud detection by establishing dynamic baselines of normal user behavior (e.g., login times, locations, device usage, and access patterns). Machine learning (ML) models continuously refine these baselines, identifying anomalies that signal potential fraud, such as impossible travel scenarios or unusual access requests. For example, AI can detect a login from a new country within minutes of a previous session elsewhere, triggering step-up authentication or alerts.
2. AI enables dynamic risk assessment by scoring authentication requests in real-time based on contextual factors (e.g., device, IP address, user behavior). This aligns with NIST SP 800-63’s Authentication Assurance Levels (AAL), where AI-driven systems apply AAL1 (e.g., passwordless login) for low-risk scenarios and escalate to AAL2 or AAL3 (e.g., phishing-resistant MFA like FIDO2) for high-risk cases, such as new device logins or sensitive transactions. AI models incorporate advanced signals, such as behavioral biometrics (e.g., typing patterns, mouse movements), and environmental data, thereby reducing user friction while enhancing security.
3. AI is evolving to predict and prevent fraud before it occurs by analyzing historical and real-time data to identify emerging threats. ML models correlate patterns across identity systems, detecting risks like credential stuffing or account takeover attempts. For example, AI can flag a spike in failed logins across multiple accounts, initiating automated responses like temporary account locks or mandatory MFA and enabling proactive measures like disabling compromised credentials or alerting SOC teams, reducing response times from hours to seconds.
4. AI enhances risk assessment by automating policy enforcement and optimizing access controls. Using frameworks like Open Policy Agent (OPA), AI-driven systems enforce Zero Trust principles, dynamically granting least-privilege access based on real-time risk scores. Additionally, ML-powered roles analyze usage patterns to recommend optimized roles, thereby reducing over-privileged accounts, a common vector for fraud. These capabilities are critical for managing machine identities in cloud environments, where non-human accounts (e.g., APIs, bots) outnumber human users and require continuous risk assessment.
5. As regulatory scrutiny increases (e.g., GDPR, NIST SP 800-53), explainable AI (XAI) is emerging to provide transparency in fraud detection and risk assessment decisions. XAI ensures that automated actions, such as flagging an account or requiring step-up authentication, are auditable and justifiable, which is critical for compliance and user trust. IAM platforms will increasingly adopt XAI to document decision-making processes, enabling organizations to meet audit requirements while maintaining robust security

Q: What advice would you give to organizations planning large-scale IAM modernization projects?

Modernizing Identity and Access Management (IAM) at scale is a complex endeavor requiring strategic planning, alignment with business goals, and a focus on security, scalability, and user experience. Large-scale IAM projects often involve consolidating disparate systems, migrating to cloud or hybrid environments, and integrating advanced technologies like AI and Zero Trust architectures, all while ensuring compliance with regulations like NIST SP 800-53, GDPR, or HIPAA. Drawing from best practices, below are some actionable advice for organizations embarking on such initiatives, incorporating insights from automation, migration strategies, and the evolving role of AI in IAM.

Begin by establishing clear goals for the modernization project, such as improving security, streamlining user onboarding, reducing operational costs, or achieving regulatory compliance. Engage stakeholders from Risk, Cyber, and business units to align IAM objectives with organizational priorities. Conduct a comprehensive assessment of the current IAM environment to identify pain points (e.g., manual provisioning, legacy systems, or siloed identities). Map these to business needs, such as enabling faster partner integrations or supporting a growing remote workforce. This ensures the project delivers measurable value while addressing high-risk areas like dormant accounts or over-privileged access.

Avoid a big-bang migration due to the risk of downtime and disruption in critical authentication systems. Implement changes incrementally using a phased rollout:

• Start with a single department or low-risk use cases to test new IAM workflows, such as automated provisioning or AI-driven authentication. Use blue-green deployments to validate performance under production conditions.
• Expand to additional user population after validating success metrics (e.g., provisioning time, login failure rates). This iterative approach minimizes risks and allows for adjustments based on real-world feedback.
• Develop robust rollback strategies, including point-in-time database recovery and DNS reversion, to ensure zero-downtime migrations. Maintain legacy systems in read-only mode during final cutovers for easy fallback.

Automation is critical for scaling IAM operations across large user bases and complex environments. Focus on automating repetitive tasks like user provisioning/deprovisioning, access reviews, and policy enforcement to reduce manual errors and ensure compliance.

Incorporate AI-driven capabilities to enhance security and user experience, particularly in fraud detection and risk assessment, as these are critical in 2025 with identity-related breaches on the rise.

Modern IAM systems must support seamless partner integrations and user onboarding while maintaining security. Use standardized protocols like SCIM, OAuth 2.0, and OIDC to simplify integrations with cloud platforms and partner ecosystems. Offer self-service portals for partners to manage credentials and APIs and implement user-centric onboarding with progressive profiling and passwordless authentication to reduce friction. Align with NIST SP 800-63’s Identity Assurance Levels for Identity Proofing.

Embed Zero Trust Architecture (NIST SP 800-207) to secure access across users, devices, and applications. Enforce least-privilege access, use short-lived tokens, and implement continuous authentication with AI-driven signals like behavioral biometrics. Automate compliance tasks (e.g., audit logs, access reviews) to meet NIST SP 800-53, GDPR, or HIPAA requirements. Use policy engines like OPA to dynamically adjust controls based on risk profiles, ensuring compliance without sacrificing performance.

Deploy AI-powered monitoring tools to track key metrics like authentication latency (p95/p99), login failure rates, provisioning times, and compliance audit pass rates. Automate alerts for anomalies (e.g., privilege escalations) and build unified threat response. Collect user and partner feedback via surveys or analytics to optimize UX and integration flows, ensuring iterative improvements. Regularly retrain AI models to adapt to new threats and maintain detection accuracy.

For large-scale migrations (e.g., legacy to cloud IAM), use strategies like blue-green deployments, and shadow traffic to ensure zero downtime. Implement backup and restore, change data capture for real-time replication with dual-write strategies for data consistency. Maintain backward-compatible APIs (e.g., versioned endpoints) and token interoperability (e.g., shared JWKS endpoints) to ensure seamless transitions for users and partners.

Address skills gaps by providing comprehensive training on new IAM tools and AI-driven features. Engage vendors for support and leverage their expertise for complex integrations. Communicate changes to stakeholders early, maintaining a real-time incident bridge during migrations to ensure coordinated decision-making. This fosters adoption and minimizes resistance to new processes.