In today’s hyper-connected digital ecosystem, enterprises rely heavily on Single Sign-On (SSO) to streamline user access across multiple applications and services. Tools like Keycloak have become the backbone for identity and access management (IAM), offering seamless authentication and authorization experiences.
However, with great power comes great responsibility. One persistent challenge remains unresolved: How can organizations selectively share user data (claims) with different client applications in a way that respects privacy, regulatory compliance, and business-specific requirements — all without compromising user experience?
The Challenge: Balancing Data Sharing with Privacy and Security
When a user logs in through an identity provider like Keycloak, a set of claims (user attributes such as email, phone number, roles) is sent to the requesting client application. But:
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Most current systems apply a one-size-fits-all approach, releasing identical claims to all clients.
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This overexposure increases privacy risks and may violate regulations like GDPR, HIPAA, or India’s DPDP.
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Different clients (e.g., HR portal vs. Marketing app) often require different subsets of user data.
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Managing claim configurations manually for dozens or hundreds of clients quickly becomes unsustainable.
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There’s no dynamic mechanism to factor in contextual signals like user consent, login device, location, or behavioral risk.
As businesses scale, this inefficiency creates serious security vulnerabilities and compliance bottlenecks, limiting the adoption of federated identity solutions in sensitive domains like finance, healthcare, and government.
The Vision: A Smart Federated Identity Hub
Imagine an identity hub that intelligently and dynamically controls which user claims to share with which client — powered by AI and enriched with contextual awareness.
Our proposed solution integrates:
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Keycloak as the core identity broker
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Custom extensions (via Keycloak SPI) that trigger an AI-powered decision engine at login
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Real-time invocation of a GenAI model (GPT-4o mini) that assesses user, client, and contextual data
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Dynamic claim release tailored to client-specific policies, user consent, and risk signals
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A Consent Ledger tracking data sharing events for auditing and compliance
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Use of MCP (Microservice Communication Protocol) for secure downstream propagation of selective claims to partner services
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Optional Explainability UI to visualize why certain claims were shared or withheld, building trust and transparency
How It Works: Context-Aware Claim Decisions
When a user initiates an SSO login:
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Keycloak invokes the AI-driven ClaimsMapper SPI, passing key information about the user, client, login context, and prior consents.
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The AI model processes this input, referencing internal policies and learned patterns, and outputs a customized list of claims allowed for release.
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Keycloak releases only the allowed claims to the client’s ID token or access token.
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All data sharing events are recorded in the Consent Ledger for future reference.
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For downstream microservices needing select user data, claims are securely synced using the MCP protocol — maintaining minimal data exposure.
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Admins or auditors can review decision rationales in a dedicated dashboard, ensuring accountability.
Unique Value Proposition
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Privacy-first: AI-powered data minimization enforces the principle of least privilege
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Scalable: Automatically adapts claim sharing for hundreds of clients across multiple business units
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Compliant: Supports evolving regulatory mandates with granular consent and audit trails
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Explainable: Builds stakeholder confidence with transparent claim decisioning
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Reusable: Applicable across industries from BFSI to Healthcare and SaaS
Tech Stack Overview
| Component | Technology |
|---|---|
| Identity & Auth | Keycloak (SPI Extensions) |
| AI Engine | GPT-4o mini (OpenAI API) |
| Backend Service | Spring Boot + WebClient |
| Database | PostgreSQL (Consent Ledger) |
| Protocol | MCP (Microservice Protocol) |
| Deployment | Docker / Calibo |
| Admin UI (optional) | React / Thymeleaf |
Sample Code Snippet: AI-Driven Claim Mapper SPI
public class AiDrivenClaimMapper implements ProtocolMapper, OIDCAdvancedClaimToTokenMapper {
@Override
public void transformAccessToken(TokenMapperContext context) {
AccessToken token = context.getAccessToken();
UserModel user = context.getUserSession().getUser();
ClientModel client = context.getClientSession().getClient();
Map<String, Object> inputPayload = Map.of(
"username", user.getUsername(),
"email", user.getEmail(),
"clientId", client.getClientId(),
"loginIp", context.getSession().getContext().getConnection().getRemoteAddr()
);
List<String> claimsToRelease = AiService.getAllowedClaims(inputPayload);
if (claimsToRelease.contains("email")) {
token.getOtherClaims().put("email", user.getEmail());
}
if (claimsToRelease.contains("phone")) {
token.getOtherClaims().put("phone", user.getFirstAttribute("phone"));
}
}
}
Conclusion
The Smart Federated Identity Hub is a game-changer for enterprises looking to reconcile seamless user experience with strict data privacy and security requirements. By combining the power of Keycloak’s extensibility with AI-driven contextual claim decisions, this solution not only improves security and compliance but also future-proofs identity management for the evolving digital landscape.
If you want to explore how AI can transform identity management in your organization or need help building a privacy-first identity architecture, reach out! The future of IAM is smart, context-aware, and explainable.
Would you like me to help prepare detailed implementation guides or demo videos next?
