Enterprises face several significant challenges with their digital immunity, which is crucial for addressing complexities in performance, reliability, resiliency, and security threats in modern operations.
The Challenge
Siloed Monitoring Systems
Enterprises often have too many systems being monitored in isolation, lacking a unified, single pane of glass view. This fragmentation hinders comprehensive oversight and efficient management.
Vendor Lock-In and Limited Observability
Reliance on third-party platforms can cause vendor lock-in and high costs. Current APM (Application Performance Management) tools often lack data correlation across sources, hindering effective problem-solving.
High Mean Time to Detect (MTTD) & Mean Time to Repair (MTTR)
Long detection and repair times harm customer experience and brand reputation. Dependency on experts for issue resolution increases risks and delays.
Lack of AI-ML Integration
Absence of AI and machine learning capabilities for proactive problem detection and elimination leaves enterprises vulnerable to emerging threats, preventing them from effectively mitigating risks.
Insufficient Fault Testing and Ransomware Attacks
Lack of proactive, experimental fault testing leads to greater exposure to vulnerabilities. Additionally, the increased frequency and sophistication of ransomware attacks cause significant network outages and operational disruptions.
What We Do
Apexon’s Digital Immunity is a comprehensive package of end-to-end offerings inspired by the human immune system, designed to enhance the resilience of enterprise IT systems, and protect digital assets more effectively. Leveraging advanced AI-ML technology, our Digital Immunity framework ensures 100% business continuity by significantly reducing system failures and security incidents.
To achieve digital immunity, organizations must embrace and build on six key practices
Our Offerings
Site Reliability Engineering (SRE)
Site Reliability Engineering (SRE) integrates software engineering practices with infrastructure and operations to create scalable and highly reliable software systems, minimizing downtime and enhancing customer experience. This practice allows for progressive experimentation without degrading the user experience and supports resilient and automated deployments with a “Shift-Left” security approach.
Key Features:
- Multi-Cloud Deployment support with the resilient, automated deployment and progressive delivery.
- Well-Architected Review framework focused on Security, Performance, Cost, Sustainability, Operational Efficiency, and Reliability.
Monitoring & Observability
Our Monitoring & Observability offering provides meaningful monitoring with integrated observability and AIOps solutions for digital-first businesses, simplifying ITOps.
Key Features:
- Instrument applications to ensure effective Metrics, Events, Logs, and Traces (MELT) data
- Compliance with Open Telemetry (OTEL) standards
- Comprehensive observability with effective thresholds, triggers, event suppression, ticket creation, and synthetic tests
- Root Cause Analysis (RCA) through causal inference
- 360-degree visibility with real-time reports and cross-event correlation
Auto Remediation(AI-Ops)
Our AI-Ops framework brings context-sensitive monitoring capabilities and automated remediation functions into applications using open-source and specialized AI partner solutions.
Key Features:
- AI-first approach for predicting and reducing failures
- Cognitive context-sensitive monitoring and automated remediation functions
- Operational resiliency and reliability with AIOps
- Patented Taral AI framework supporting RCA and auto-healing
Chaos Engineering
Chaos Engineering involves fault injection testing to uncover vulnerabilities and weaknesses within complex systems, ensuring robustness and reliability.
Key Features:
- Fault-tolerant stack and fast-fail approach for distributed systems and landscapes
- SLO/SLA impacts and visibility dashboards
- Extensibility to infrastructure, network, and applications
AI-Augmented Testing
Our AI-Augmented Testing framework employs AI/ML-based technologies and practices to make software testing independent from human intervention, enhancing efficiency and accuracy.
Key Features:
- Zero-touch testing approach driven by AI/ML
- Complexity reduction with impact assessment and accelerated savings
- Generative AI testing
Cyber Security
Cyber Security within Apexon’s Digital Immunity framework focuses on securing the software supply chain and enabling secure frameworks, policies, and procedures to counter threats.
Key Features:
- Security incidents prediction and prevention
- Continuous proactive testing to secure the software supply chain
Tools/Accelerators
Apexon LZA for Cloud, Compass, Progressive Delivery, CI/CD Release Pipeline, Well-Architected Review Framework, FinOps
By integrating these six offerings, Apexon’s Digital Immunity framework transforms the enterprise landscape, ensuring robust and resilient IT infrastructure that can withstand and quickly recover from any disruptions
More information about our expertise in Digital Immunity Services in following fact sheets
Apexon Innovation Labs – Digital Twins
Why Apexon
KPIs We Deliver
67% reduction in MTTR (mean time to repair)
Mean time between incidents
30% reduction in over-provisioning
92% alert compression through correlation
Reduce system outages by 50%
Predict outages and increase productivity by 10%
96% reduction in false alerts
Apexon’s Digital Immune System (DIS)Assessment and Maturity Model
Apexon’s Digital Immune System (DIS) Assessment and Maturity Model guides enterprises through a comprehensive transformation journey. This journey can be initiated during or after a digital transformation process, ensuring robust and resilient IT systems. The process involves a multi-faceted approach starting with an assessment of the current IT landscape, followed by analysis, design, development, implementation, and transition to support.
The transformation journey is a collaborative effort involving Solution Architects, Application Engineers, Cloud Engineers, and DevOps Engineers to ensure success. The duration varies based on the specific nature of the engagement, following a fully agile timeline:
Assess: 4 – 6 Weeks
Analyze: 2 Weeks
Develop: 6 – 8 Weeks
Implement: 8 – 10 Weeks
Operate: Ongoing
Transformation Tracks
The choice of transformation path depends on customer requirements and priorities. Here are three sample tracks:
| TRACK | OBJECTIVE | APPROACH |
|---|---|---|
| Track 1: Current Platform Transition, Foundation for AIOps | Safely replace the existing platform with a proven solution. |
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| Track 2: Leverage AI Patents for Delivering Business Impact | Reduce Mean Time to Repair (MTTR) using actionable insights. |
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| Track 3: Informed Experiments, Targeted Augmentation | Triage hotspots and augment solutions as needed. |
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