MOSIP Sentinel AI - an AI agent for proactive monitoring, alerting, reporting, and assisted remediation of the MOSIP platform.

[Varaniya201]

Problem Statement / Description

MOSIP is a modular, distributed identity platform composed of many microservices (Registration Processor, ID Authentication, ID Repo, Partner Management, Keymanager, Kernel services, etc.) running on Kubernetes alongside supporting infrastructure (PostgreSQL, Kafka, Keycloak, object store, HSM, Redis) and integrated third-party systems (ABIS and biometric SDK providers, notification gateways, external HSM, and registered authentication/eKYC/credential partners).

Today, operational issues are detected reactively. Teams rely on dashboards and manual log inspection, and problems often surface only after a service has already degraded or failed. Several failure modes are recurring and high-impact:

• Certificate and key expiry — TLS certificates, partner signing/encryption certificates, MOSIP PKI certificates, and HSM/Keymanager keys can silently expire, breaking ID authentication, partner integrations, or inter-service communication.
• Pods/workloads down — services in CrashLoopBackOff, OOMKilled, Pending, or below their expected replica count can degrade or disable platform functionality.
• Third parties not working as expected — external dependencies and partner systems can become unreachable, error out, or breach SLAs, causing failures that are hard to distinguish from internal faults.

Diagnosing these requires an experienced engineer to correlate logs and signals across many services and decide on a fix. That expertise is scarce, slow, and inconsistent — and the impact differs by team: developers lose time debugging unstable lower environments, System Integrators risk go-live and production outages, and the dissemination team risks failures during high-visibility pilots and demos. There is also no consolidated, periodic view of platform health and recurring issues to support informed decisions across these teams.

There is a need for an AI-driven observability assistant that watches each MOSIP environment around the clock, understands MOSIP's components and failure patterns, predicts and detects discrepancies, reports on health and trends, recommends mitigations grounded in known runbooks, and — with a human in the loop — executes approved fixes safely and auditably.

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User Story

As a member of any team running MOSIP in an environment — an internal developer/DevOps engineer working through SDLC stages, a System Integrator standing up and operating a production deployment, or a dissemination team member running a pilot —

I want an AI agent that continuously monitors my MOSIP environment's logs, metrics, workload health, and infrastructure signals, proactively flags discrepancies and impending failures (certificate/key expiry, pods down, third-party integrations misbehaving), reports on overall health and trends, notifies the right person with a proposed fix, and applies it on my confirmation,

so that I can maintain the sanity of the setup and prevent uninformed, surprise issues — reducing mean-time-to-detect (MTTD) and mean-time-to-resolve (MTTR) across development, integration, and pilot/production environments.

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Personas / Target Users

The tool serves any team responsible for running or standing up MOSIP in an environment, across the full lifecycle:

• Internal Developers & DevOps — run MOSIP across SDLC environments (dev, build/CI, test/QA, staging). They use the AI to catch configuration, integration, and infrastructure issues early — before code or builds are promoted — and to keep lower environments healthy and reproducible.
• System Integrators (SIs) — carry out production-grade setup and deployment for a customer or country. They use the AI to validate production readiness, monitor go-live, and continuously maintain the sanity of the production setup at scale (certs/keys, pod health, third-party integrations).
• Dissemination Team — run pilots, PoCs, and demos. They use the AI to keep pilot/sandbox environments stable during pilot programs and stakeholder demonstrations, avoiding surprise failures at critical moments.

Across all three, the shared goal is the same: maintain the sanity of the MOSIP setup and surface issues proactively, before they become uninformed surprises.

Goals

• Continuously ingest and analyze MOSIP logs, metrics, and infrastructure signals on a scheduled/streaming basis.
• Proactively detect impending certificate and key expiry (TLS, partner certs, MOSIP PKI, Keymanager/HSM keys) with enough lead time to act before failure.
• Verify the runtime health of all MOSIP workloads and detect when any pods are down, crash-looping, or below their expected replica count.
• Monitor the health and responsiveness of third-party and external dependencies and detect when they are unavailable, erroring, or breaching expected SLAs.
• Detect broader anomalies and discrepancies across services (errors, resource exhaustion, latency spikes, queue lag, integration failures).
• Be multi-environment aware (dev, test, staging, production, pilot/sandbox), with per-environment thresholds, routing, and permitted remediation actions.
• Notify the correct stakeholder for each issue type via their preferred channel, with clear context (what, where, severity, likely impact).
• Provide reporting and analytics — dashboards, periodic health summaries, expiry forecasts, incident/remediation history, KPIs (MTTD/MTTR), and trend analysis — for informed decision-making across personas and environments.
• Propose a concrete, MOSIP-aware mitigation for each detected issue.
• Execute the proposed fix only after explicit human confirmation, with full auditability and rollback awareness.
• Reduce MTTD and MTTR for common infrastructure issues and eliminate certificate/key-expiry-related outages.

Non-Goals

• Not a replacement for MOSIP's existing observability stack (Prometheus, Grafana, Elasticsearch/Kibana, Alertmanager); it augments and consumes from these.
• Not fully autonomous remediation — sensitive or high-blast-radius actions always require human approval; there is no "self-healing" without confirmation in this phase.
• Third-party failures are detect-and-escalate by default, not detect-and-remediate — the agent notifies/escalates to the owning vendor or partner (and may trigger a configured fallback) rather than attempting to fix systems it does not control.
• Not an application/business-logic debugger — it does not fix functional code defects, correct identity data, or alter business rules.
• Not a SIEM or security incident response platform, though it may surface security-relevant infra signals.
• Not intended to monitor non-MOSIP systems in the initial release.
• Does not provision new infrastructure or perform capacity planning/cost optimization (future scope).

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Acceptance Criteria

1. Monitoring cadence — The agent runs on a configurable schedule (and/or streams) and processes signals from defined MOSIP sources without manual triggering.
2. Expiry detection — Given a certificate or key approaching expiry, the agent raises an alert at a configurable threshold (e.g., 30/14/7 days out) identifying the exact artifact, owning service, and impact.
3. Pod/workload health — When any MOSIP pod is down, in CrashLoopBackOff/OOMKilled/Pending/ImagePullBackOff, failing readiness/liveness probes, or a deployment is below its desired replica count, the agent detects it, identifies the affected service, and raises a severity-classified alert.
4. Third-party availability — When an external dependency or partner integration stops responding as expected (timeouts, elevated error rates, failed health checks, SLA/latency breach, circuit-breaker trips), the agent detects it and attributes it to the specific dependency.
5. Fault attribution — Each alert clearly states whether the root cause is a MOSIP pod/service, a platform infra component, or a third party, so the right team is notified.
6. Discrepancy detection — When other known issue patterns occur (e.g., DB connection failure, Kafka lag breach, disk pressure, HSM disconnect), the agent detects and classifies severity.
7. Environment awareness — The agent operates per environment (dev, test, staging, production, pilot), applying environment-specific thresholds, alert routing, and remediation permissions.
8. Targeted notification — Each alert is routed to the mapped stakeholder/team via the configured channel (email, Slack/Teams, and/or a ticket in Git, email notification) with a human-readable summary and root-cause hypothesis.
9. Reporting & analytics — The agent provides health dashboards and generates scheduled/on-demand reports covering platform health, upcoming cert/key expirations, incident and remediation history, operational KPIs (MTTD/MTTR, alert volume, SLA compliance), and recurring-issue trends — viewable per environment/persona and exportable (e.g., PDF/CSV).
10. Mitigation proposal — Every alert includes a specific recommended action drawn from a runbook/knowledge base, plus expected outcome and risk level. For third-party issues, the recommended action is escalation/fallback rather than a direct fix.
11. Confirm-then-act — The agent executes a remediation only after an authorized user explicitly approves it; it never acts on its own for fix actions.
12. Audit trail — Every detection, notification, proposal, approval, and executed action is logged immutably (who, what, when, result) for compliance review.
13. Safety guardrails — Remediation actions respect RBAC; high-risk actions are blocked or require elevated approval; failed actions surface a clear status and next steps.
14. No false-silence — Acknowledged issues are tracked to closure; unresolved/recurring issues are escalated.

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Features & Sub-features

1. Data Ingestion & Integration

Connect to and collect signals from the MOSIP environment.

• Log & metric connectors (Elasticsearch/Kibana, Prometheus/Grafana, Loki)
• Kubernetes events & API integration
• Alertmanager integration
• Component/endpoint health probes
• Multi-environment data sources (dev, test, staging, production, pilot)

2. Monitoring & Detection

Watch for issues across the platform.

• Certificate & key expiry monitor (TLS, partner certs, MOSIP PKI, Keymanager/HSM keys)
• Pod & workload health monitor (down, CrashLoopBackOff, OOMKilled, replica drift, probe failures)
• Third-party & dependency health monitor (ABIS, biometric SDKs, gateways, partners, object store, Keycloak)
• Anomaly & discrepancy detection engine (rules + ML/pattern based)
• Infrastructure resource monitoring (disk, CPU/memory, DB connections, Kafka lag, HSM connectivity)

3. Analysis & Intelligence

Turn raw signals into understanding.

• Root-cause analysis & cross-service correlation
• Fault attribution (internal service vs. platform infra vs. third party)
• Severity classification & SLA mapping
• Predictive/proactive detection (expiry forecasting, trend-based early warning)

4. Notification & Alerting

Get the right issue to the right person.

• Stakeholder routing & ownership mapping (per issue type and persona)
• Multi-channel notification (email, Slack/Teams, SMS, Jira/ServiceNow tickets)
• Severity-based escalation
• Alert de-duplication & noise reduction

5. Remediation (Human-in-the-Loop)

Fix issues safely on confirmation.

• Mitigation recommendation engine (runbook-grounded fixes)
• Guarded action executor (kubectl/Helm, Keymanager/cert-renewal APIs)
• Approval workflow & RBAC
• Dry-run/preview & rollback awareness
• Third-party escalation/fallback handling (detect-and-escalate)

6. Reporting & Analytics

Visibility, accountability, and trends.

• Real-time health & status dashboards (per environment)
• Scheduled health-summary reports (daily/weekly/monthly)
• Certificate & key expiry forecast report (upcoming expirations and lead time)
• Incident & remediation history report (what happened, what was done, by whom)
• Operational KPIs (MTTD, MTTR, alert volume, false-positive rate, SLA compliance)
• Trend & recurring-issue analytics (top offenders, repeat failures, hotspots)
• Audit & compliance reports (immutable record of detections, approvals, actions)
• Per-persona / per-environment views (developer, SI, dissemination)
• Exportable & auto-delivered reports (PDF/CSV, email/scheduled distribution)

7. Knowledge & Learning

Improve over time.

• Runbook & knowledge base (MOSIP failure patterns and resolutions)
• Feedback loop (operators rate proposals and outcomes)
• Continuous learning to improve detection accuracy and recommendations

8. Platform & Governance

Operate the tool itself safely.

• Multi-environment management (per-env thresholds, routing, permissions)
• Configuration & policy management (schedules, channels, auto-eligible vs. confirmation-only actions)
• Audit & compliance logging
• Operator interface (dashboard + conversational/chat)
• Security & access control (RBAC, least privilege)