AIOps that detects, decides and acts under your control

Track 01

SRE AIOps

Reliability, SLOs, incident intelligence, predictive operations and self-healing across hybrid environments.

Explore SRE AIOps

• Autonomous SRE & AIOps Command Center

  Unify multi-site and cloud telemetry, correlate incidents, find probable root cause and coordinate safe remediation.

• AI Self-Healing Infrastructure

  Detect recurring failures and run policy-controlled remediation with verification.

• Predictive SRE: Failure & Capacity Forecasting

  Forecast capacity exhaustion and SLO risk before static thresholds are breached.

• ChangeGuard: Change Risk & Deployment Reliability

  Score code, IaC and Kubernetes changes, control progressive delivery and roll back unhealthy releases.

• Resilience, DR & Chaos Engineering

  Prove DR readiness, backup restorability, failover and RTO/RPO through safe experiments.

• Example scenario

  A faulty release is injected. Several symptoms collapse into one incident, a rollback is approved and SLO recovery is verified.

Track 02

Cybersecurity AIOps

AI-assisted threat detection, security-event correlation, behavioral analytics and governed response.

Explore Cybersecurity AIOps

• SOC Threat Correlation & Incident Response

  Turn SIEM, EDR, cloud, firewall and identity alerts into prioritized incidents with guided response.

• Vulnerability Prioritization & Remediation

  Rank vulnerabilities by exploitability, exposure, runtime context and asset criticality.

• Identity & UEBA Detection and Response

  Spot suspicious user and service-account behavior against learned login and privilege baselines.

• Cloud & Kubernetes Threat Containment

  Detect runtime and control-plane threats and apply tightly scoped containment.

• Phishing, Malware & Security Triage

  Automate safe enrichment, classification and case creation for suspicious messages, files and URLs.

• Example scenario

  Credential abuse followed by lateral movement is simulated. Raw events collapse into one incident with a guided response.

Track 03

GRC AIOps

Continuous compliance, automated evidence, risk analytics and policy-as-code across hybrid infrastructure.

Explore GRC AIOps

• Continuous Compliance & Auto-Remediation

  Test cloud, Kubernetes, Linux and IaC against mapped controls and fix approved violations.

• EvidenceOps: Audit Evidence & Control Testing

  Replace manual evidence collection with scheduled collectors and automated control tests.

• Risk Register & Control Gap Prediction

  Update operational risk from incidents, vulnerabilities, failed controls and asset criticality.

• Policy-as-Code & Drift Management

  Turn governance requirements into executable policy for CI/CD, cloud and Kubernetes.

• Third-Party & Vendor Risk

  Track vendor risk using attestations, SLA incidents, questionnaires and approved external inputs.

• Example scenario

  A known violation is introduced, mapped to controls and remediated, with before and after evidence.

Track 04

Network AIOps

Topology-aware observability, prediction, root-cause analysis and safe automation across WAN, cloud and Kubernetes networking.

Explore Network AIOps

• Global Network AIOps Command Center

  Unify health, topology, incidents and automation for enterprise, cloud and Kubernetes networks.

• Predictive WAN / SD-WAN & Path Optimization

  Forecast path degradation from latency, loss, jitter and utilization and recommend safe path changes.

• Network Root Cause Analysis & Self-Healing

  Suppress downstream symptoms, rank the probable root cause and run approved repair workflows.

• Network Security Policy Drift

  Detect risky firewall, security-group and network-policy drift and conflicting rules.

• Kubernetes & Service Mesh Network AIOps

  Correlate CNI, DNS, ingress, NetworkPolicy and mesh signals to diagnose cluster networking failures.

• Example scenario

  One upstream failure raises many alerts. The actual root cause is identified and the downstream symptoms are suppressed.

Track 05

CloudOps AIOps

Hybrid and multi-cloud inventory, health analytics, drift intelligence, workload placement and governed remediation.

Explore CloudOps AIOps

• Hybrid & Multi-Cloud Operations Control Plane

  One operations layer for AWS, Azure, GCP and private-cloud inventory, health, incidents, changes and automation.

• Cloud Resource Health & Auto-Remediation

  Detect unhealthy cloud resources and recover them under policy control.

• Configuration Drift & Governance Remediation

  Compare cloud state with desired IaC and policy and safely reconcile drift.

• Multi-Cloud Capacity & Workload Placement

  Recommend placement from capacity, latency, availability, policy and cost inputs, with reasons.

• Service Dependency & Outage Impact

  Map applications to cloud dependencies, correlate provider events and estimate blast radius.

• Example scenario

  Console drift is created, then the intended state is restored through Git and IaC.

Track 06

FinOps AIOps

Cost anomaly detection, rightsizing, forecasting and allocation that respect reliability targets.

Explore FinOps AIOps

• Cost Anomaly Detection & Auto-Guardrails

  Detect abnormal spend early and trigger governed cost-control workflows.

• Kubernetes Rightsizing & Efficiency

  Tune CPU and memory requests, limits and replicas from real utilization while protecting SLOs.

• Commitment & Savings Forecasting

  Forecast stable usage and model commitment scenarios with risk buffers.

• Showback, Chargeback & Unit Economics

  Allocate shared cost and calculate cost per service, customer, transaction or team.

• GreenOps: Cost, Carbon & Performance

  Balance cost, performance, utilization and sustainability signals within SLO limits.

• Example scenario

  A synthetic spend spike is injected. It is detected, attributed and met with a low-risk guardrail.

Track 07

DevSecOps AIOps

Software-delivery reliability, AppSec prioritization, supply-chain protection and secure release automation.

Explore DevSecOps AIOps

• CI/CD Pipeline Reliability & Root Cause

  Classify pipeline failures, spot flaky and transient issues and recommend safe recovery.

• Application Security Risk Prioritization

  Deduplicate and rank SAST, DAST, dependency, container and IaC findings by exploitability and context.

• Software Supply Chain Security

  Track SBOMs, provenance, artifact signatures, dependencies and build integrity.

• Secure Release Orchestration

  Combine tests, security, IaC policy, live SLO health and change risk into one governed release gate.

• Secrets, IAM & Access Misconfiguration Prevention

  Find exposed secrets and excessive permissions across code, CI/CD, Kubernetes and cloud configuration.

• Example scenario

  A healthy release and a risky one are compared and receive different promotion outcomes.

Track 08

MLOps / LLMOps AIOps

Reliability, observability, governance and capacity for ML models, LLM applications and AI agents.

Explore MLOps / LLMOps AIOps

• Model Reliability & Production Operations

  Operate model endpoints with SLOs, quality signals, version context and safe rollback.

• LLM & AI Agent Observability and Guardrails

  Trace prompts, tokens, tool calls and agent steps while enforcing operational policy.

• GPU & Inference Capacity Optimization

  Forecast inference demand and tune scaling, batching and placement while protecting latency.

• Model & Data Drift with Automated Retraining

  Detect feature, prediction and performance drift and trigger validation and retraining pipelines.

• AI Governance, Prompt Security & Model Risk

  Monitor model, prompt and tool policy compliance, data handling and AI-system risk.

• Example scenario

  An agent attempts a disallowed action. Policy intercepts it and the trace shows exactly why.