EMACH.ai Open Source Finance Platform

 What Emach.ai Offers

1. An AI‑Driven, Open Finance Platform

Emach.ai is built using “first principles thinking” and delivers a composable, API-first, event-driven, microservices-based platform.

It includes over 329 microservices, 1,757 APIs, and 535 events, allowing banks to build customized solutions across customer segments—from retail and SME to corporate and HNI clients.  

2. Customer-Centric Architecture

Designed as a headless, cloud-native ecosystem that supports seamless integration with existing core banking systems, fintech partners, third-party services, and regulatory infrastructure.  

Focused on delivering personalized financial journeys across acquisition, engagement, up-selling, and retention via embedded AI for intelligent codeless personalization.  

3. Modular Solutions for Financial Institutions

Emach.ai offers several key modules:

eMACH.ai Sigma: A reporting and analytics tool enabling business users without technical expertise to create and customize dashboards and reports on the fly. It features scheduling, filtering, merging multiple reports, and exporting in various formats.  

eMACH.ai Lending (iKredit360): A comprehensive credit platform that supports omnichannel lending workflows—from origination and servicing to fraud and loyalty management—across retail, corporate, SME, and agri-business.  

eMACH.ai DEP (Digital Engagement Platform): A tool to enhance customer engagement by creating codeless digital journeys with AI-powered personalization, scalable cloud-native infrastructure, and seamless integration.  

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​ Why Banks Use It

Scalable & Agile Architecture: Its microservices and APIs allow rapid deployment, enhancement, and integration without disrupting legacy systems.  

Embedded AI: Helps automate fraud detection, personalize customer service, and provide predictive insights.  

Enterprise-Grade Security: Designed with banking-level security controls to meet regulatory and compliance requirements.  

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​ In Summary

Emach.ai is marketed as one of the world’s most comprehensive open finance platforms, enabling banks to operate future-ready, customer-focused services by combining AI, API-first architecture, event-based design, and modularity . Its primary modules—Sigma, Lending (iKredit360), and Digital Engagement Platform—address financial reporting, credit lifecycle, and customer engagement respectively.

EPOCH SCORE

 Here's a concise summary of the MIT Sloan paper:

📄 “The EPOCH of AI: Human–Machine Complementarities at Work”

by Isabella Loaiza & Roberto Rigobon, 2024

🧠 What Is the EPOCH Index?

EPOCH is a human-centric framework designed to identify which human skills are least likely to be replaced by AI and most likely to be augmented by it.

🔤 EPOCH stands for:

Letter Human Capability

E Empathy and Emotional Intelligence

P Presence, Networking, and Connectedness

O Opinion, Judgment, and Ethics

C Creativity and Imagination

H Hope, Vision, and Leadership

These 5 clusters are considered core human attributes that AI cannot easily replicate.

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📊 What Did the Paper Do?

The researchers evaluated hundreds of job tasks using three main metrics:

1. 🧠 EPOCH Score

How strongly the task depends on those uniquely human qualities.

2. ⚠️ Risk of Substitution

How easily the task could be replaced by AI.

3. 🔄 Potential for Augmentation

How well the task could be enhanced or supported by AI, without full replacement.

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🔍 How Did They Analyze Tasks?

They used data from the O*NET database (by the U.S. Department of Labor).

O*NET breaks down every job into individual work activities (IWAs).

Each IWA (e.g., “teach others,” “resolve conflict,” “repair machinery”) was scored based on:

Its EPOCH alignment

AI substitution risk (via LLM capabilities)

Potential for augmentation

💼 What Were the Findings?

1. Jobs with High EPOCH Scores are less likely to be automated.

Examples:

Therapists, Teachers, Social Workers, CEOs

Roles requiring leadership, empathy, ethical decision-making.

2. Jobs with Low EPOCH but High Technical Skills are more likely to be replaced.

Examples:

Data entry, basic coding, routine analysis.

3. AI augments human roles better when the task involves EPOCH skills.

AI can support, but not replace human leadership, emotional connection, and vision.

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💡 Key Insight

> "The future of work isn't about competing with AI, but complementing it by emphasizing what makes us human."

🎯 Summary in 1 Sentence

The EPOCH index helps identify human capabilities that are least automatable, guiding workers and companies to invest in skills that AI can't easily replace—and shows how AI can be used to support rather than substitute us.

AI Villain Vs Cybersecurity Hero

 

Timeline of Cyber Incidents and Their Economic Costs (2000-2031)

This timeline highlights key findings and projections regarding the economic impact of cyber incidents as presented in the provided sources.

Early 2000s:

• 1996-2000 (US): Limited evidence of overall negative stock market effects from information security breaches, though breaches involving unauthorized access to confidential data show significant impact (Campbell et al., 2003).
• 1996-2001 (US): Firms lose an average of 2.1% of their market value within two days of a cybersecurity breach announcement (Cavusoglu, 2004).
• 1996-2002 (US): All IT security breaches yield negative market returns; credit card theft (9-15%) and DoS incidents (1-4%) have the most significant impact (Garg et al., 2003).
• 1997-2002 (US): Companies relying on websites for business operations experience negative stock market returns following DoS attacks; non-internet specific companies do not (Hovav and D’Arcy, 2003).
• 1997-2003 (US): No significant negative market returns to information security breach announcements (Kannan et al., 2007).
• 1997-2008 (US): Firms disclosing risk-mitigating information in financial reports are less likely to have incidents and are punished less severely by the market when breaches occur (Wang et al., 2013).
• 1999-2006 (US): A significantly negative but short-lived stock market effect is observed for privacy breach events (Acquisti et al., 2006).

2010s:

• 2002-2018 (US): Large data breaches are associated with a 5-9% loss of reputational intangible capital or brand value (Makridis, 2021).
• 2004 (US): Privacy Rights Clearinghouse (PRC) begins providing open data on public cyber breaches by government entities, becoming a key data source for academic studies.
• 2005-2016 (US): Firms experience a 1.5–1.9% reduction in cumulative abnormal returns in 30 days after a breach; higher pre-event investment in corporate social responsibility reduces losses (Akey et al., 2021).
• 2005-2016 (US): Corporate bond values decrease following a breach announcement, with bondholders losing approximately 2% of wealth within one month (Iyer et al., 2020).
• 2005-2017 (US): Disclosure of a cyberattack significantly reduces shareholder wealth and sales growth; shareholder wealth decreases by 1.09% within 3 days (Kamiya et al., 2021).
• 2005-2017 (US): Sales growth for large firms, especially in retail, declines significantly for 3 years after a cyberattack (Kamiya et al., 2021).
• 2005-2017 (US): Firms hold more cash after a cyberattack; suppliers and unaffected peer firms also increase cash holdings (Garg, 2020).
• 2005-2018 (US): Cumulative abnormal returns of breached firms decrease by 0.46% within 5 days of a breach (Piccotti and Wang, 2022).
• 2005-2018 (US): Hackers initiate informed trading up to 12 months prior to data breach announcements in options markets; insiders initiate 4 months prior (Piccotti and Wang, 2022).
• 2005-2018 (US): Abnormal trading activity before breach announcements suggests short sellers exploit insider knowledge (Wang et al., 2022).
• 2008-2019 (US): Firms with higher cybersecurity risk outperform others in regular times, but incur large losses after a breach disclosure (Florackis et al., 2023).
• 2010-2015 (US): Managers disclose cyberattack information when investors already suspect high probability; withheld (disclosed) attacks associated with 2.6% (0.7%) decrease in equity values (Amir et al., 2018).
• 2011 (UK): UK government estimates cybercrime costs USD 33.67 billion (1.3% of GDP), with businesses bearing 77.78% (UK Cabinet Office, 2011).
• 2011-2016 (US): Insider trading occurs 55-72 days before breach announcements, saving insiders an average of USD 35,000 (Lin et al., 2020). Stock prices decrease by 1.18% in a 3-day window, 1.44% in a 5-day window, 1.26% in a 21-day window, and 1.44% in a 41-day window after a breach announcement (Lin et al., 2020).
• 2011-2020 (Global): The rise of cybercrime is positively correlated with the performance of cybersecurity companies (Lhuissier and Tripier, 2021).
• 2012-2018 (Global): Hacking campaigns increase target institutions' exposure to the deep and dark web by 62% per year during the first two years after the campaign's start (Keppo and Niemela, 2021).
• 2014 (Ireland): Total cost of cybercrime in Ireland is USD 695.5 million (Grant Thornton, 2021).
• 2015 (Brazil): Cybercrime costs in Brazil estimated at around USD 8 billion (0.4% of GDP).
• 2015 (Global): Cybercrime damage costs are USD 3 trillion (eSentire & Cybersecurity Ventures, 2022).
• 22016 (Kenya): Cybercrime costs the Kenyan economy USD 36 million (0.05% of GDP) (Interpol, 2016).
• 2016 (South Africa): Cybercrime costs the South African economy USD 573 million (0.17% of GDP) (Interpol, 2016).
• 2016 (Nigeria): Cybercrime costs the Nigerian economy USD 500 million (0.12% of GDP) (Interpol, 2016).
• 2017 (Global): Consumers worldwide lose USD 172 billion due to cybercrime (Norton, 2017).
• 2017 (Global): Enterprises incur an average direct cost of USD 551,000 to recover from a security breach; SMBs face USD 38,000 (Kaspersky Lab & B2B, 2017).
• June 2017 (Ukraine): The NotPetya attack, a major cyberattack, occurs (Crosignani et al., 2023).
• 2017-2018 (Global): NotPetya causes at least USD 7.3 billion in losses by affected customers, four times larger than direct losses (Crosignani et al., 2023).
• 2017 (Global): Ransomware damages surge to USD 5 billion, a 15-fold jump from 2015 (eSentire & Cybersecurity Ventures, 2022).
• 2017-2021 (Global): Average cost of a ransomware incident is USD 623,000 for SMEs and USD 29.6 million for large companies (NetDiligence, 2022). Ransomware incidents make up 87% of claims with a business interruption (BI) component for SMEs (NetDiligence, 2022).
• 2018 (Global): Global cost of cybercrime increases to USD 600 billion (McAfee & Center for Strategic and International Studies (CSIS), 2018).
• 2018 (Germany): Over half of all German companies victims of cybercrime, causing damages of over USD 64 billion per year (McAfee & Center for Strategic and International Studies (CSIS), 2018).
• 2018 (Global): Ransomware damages projected to reach USD 8 billion (eSentire & Cybersecurity Ventures, 2022).
• 2018-2019 (Global): Ransomware infections decrease by over 20% overall, but enterprise detections rise by 12% (Symantec, 2019).
• 2019 (Global): Ransomware damages projected to reach USD 11.5 billion (eSentire & Cybersecurity Ventures, 2022).
• 2019 (Global): Average cost of a cyber claim rises to USD 369,000 (Willis Towers Watson, 2020). Ransomware events increase significantly due to low investment, low risk, high reward (Willis Towers Watson, 2020).
• By 2024 (Global): Companies face the risk of losing an estimated USD 5.2 trillion in value creation opportunities from the digital economy due to cyberattacks (Accenture & Ponemon Institute, 2019).

2020s and Projections:

• 2020 (Ireland): Cost of cybercrime dramatically increases to USD 10.5 billion (Grant Thornton, 2021).
• First half of 2020 (Ireland): Irish consumers lose over USD 13.2 million to debit and credit card fraud (Grant Thornton, 2021).
• 2020 (Global): Average time to identify and contain a cybersecurity breach is 280 days, with an average cost of nearly USD 4 million in losses and remediation (CheckPoint – Software Technologies LTD, 2021).
• 2020 (Africa): More than 90% of African businesses operate without proper cybersecurity protocols (CGTN, 2020).
• 2021 (Africa): Cybercrime reduces Africa’s GDP by more than 10%, amounting to an estimated loss of USD 4.12 billion (Phys.org, 2021).
• 2021 (Global): Ransomware damages projected to reach USD 20 billion (eSentire & Cybersecurity Ventures, 2022).
• 2021 (Global): Average cost of a data breach for SMEs is USD 105,000; for enterprises, it falls to USD 927,000 (Kaspersky Lab, 2021).
• 2021 (Global): Cybersecurity budgets for enterprises drop significantly by 19% (USD 14 million to USD 11.4 million); SMBs experience a minor decrease of 3% (USD 275,000 to USD 267,000) (Kaspersky Lab, 2021).
• 2021 (US): The U.S. Secret Service prevents over USD 2.3 billion in economic losses from cyber incidents in the previous fiscal year (Verizon, 2022).
• 2022 (Global): Average data breach cost reaches an all-time high of USD 4.35 million, a 2.6% increase from 2021 (IBM, 2022). Healthcare breach costs reach a record high of USD 10.10 million (IBM, 2022).
• 2022 (Global): Ransomware breach costs slightly decrease to USD 4.54 million from USD 4.62 million in 2021, but occurrence increases by 11% (IBM, 2022).
• 2022 (Global): Fully deployed security AI and automation associated with significantly lower average breach costs (USD 3.15 million) compared to organizations without these measures (USD 6.20 million) (IBM, 2022).
• 2022 (Global): Ransomware dominates the threat landscape, doubling the volume of attacks from 2021 and resulting in an estimated total cost exceeding USD 20 billion (Cybereason, 2022).
• 2023 (Projection): Annual global cost of cybercrime predicted to reach USD 8 trillion (eSentire & Cybersecurity Ventures, 2022).
• By 2025 (Projection): Global cybercrime damage costs projected to reach USD 10.5 trillion annually (eSentire & Cybersecurity Ventures, 2022). Crypto crime worldwide estimated to reach USD 30 billion per year (eSentire & Cybersecurity Ventures, 2022).
• By 2031 (Projection): Ransomware attacks expected to cause damages of approximately USD 265 billion per year (eSentire & Cybersecurity Ventures, 2022).

Cast of Characters

This list includes individuals identified as authors or contributors to the academic and industry reports cited in the provided sources.

• Estefania Vergara Cobos: Economist at the Infrastructure Chief Economist Office, World Bank. Co-author of "A Review of the Economic Costs of Cyber Incidents."
• Selcen Cakir: Assistant Professor of Economics at Bogazici University. Co-author of "A Review of the Economic Costs of Cyber Incidents."
• Stephane Straub: Acknowledged for comments and support on "A Review of the Economic Costs of Cyber Incidents."
• Christine Zhenwei Qiang: Acknowledged for comments and support on "A Review of the Economic Costs of Cyber Incidents."
• Casey Torgusson: Acknowledged for comments and support on "A Review of the Economic Costs of Cyber Incidents."
• Gordon, L. A.: Co-author of multiple academic papers cited in the review, including "The economic cost of publicly announced information security breaches: empirical evidence from the stock market" (2003) and "Integrating cost–benefit analysis into the NIST Cybersecurity Framework via the Gordon–Loeb Model" (2020), focusing on the economics of information security investment.
• Loeb, M. P.: Co-author with Gordon on various works, contributing to the understanding of economic costs and cybersecurity investments.
• Romanosky, S.: Co-author of "Content analysis of cyber insurance policies: How do carriers price cyber risk?" (2019) and "Examining the costs and causes of cyber incidents" (2016), researching cyber insurance and incident costs.
• U.S. CISA: The U.S. Cybersecurity and Infrastructure Security Agency. Referenced for reports on the cost of cyber incidents.
• Corbet, S.: Co-author of "What the hack: Systematic risk contagion from cyber events" (2019), exploring systemic risk in financial markets due to cyber events.
• Gurdgiev, C.: Co-author with Corbet on the systemic risk of cyber events.
• Jamilov, R.: Co-author of "The anatomy of cyber risk" (2021), investigating firm-level cyber risk as a source of systematic risk.
• Amir, E.: Co-author of "Do firms underreport information on cyber-attacks? Evidence from capital markets" (2018), examining firms' disclosure behaviors regarding cyberattacks.
• Akey, P.: Co-author of "Hacking corporate reputations" (2021), studying the impact of data breaches on corporate reputations and social responsibility.
• Kamiya, S.: Lead author on "Risk management, firm reputation, and the impact of successful cyberattacks on target firms" (2021), which builds a model of optimal cybersecurity risk and analyzes sales growth after attacks.
• Lending, C.: Co-author of "Corporate governance, social responsibility, and data breaches" (2018), linking governance and social responsibility to data breaches.
• Piccotti, L. R.: Co-author of "Informed trading in the options market surrounding data breaches" (2022), investigating insider trading activities prior to breach announcements.
• Wang, H. E.: Co-author with Piccotti and others on papers discussing informed trading and market reactions to breaches.
• Lin, Z.: Lead author of "Insider trading ahead of cyber breach announcements" (2020), detailing the financial gains from insider trading before breach disclosures.
• Tosun, O. K.: Author of "Cyber-attacks and stock market activity" (2021), analyzing the short and long-term effects of cyberattacks on stock market value.
• Campbell, K.: Lead author of "The economic cost of publicly announced information security breaches: empirical evidence from the stock market" (2003), an early empirical study on cyber breach impact.
• Hovav, A.: Co-author of "The impact of denial‐of‐service attack announcements on the market value of firms" (2003), focusing on DoS attacks.
• D'Arcy, J.: Co-author with Hovav on the impact of DoS attacks.
• Acquisti, A.: Lead author of "Is there a cost to privacy breaches? An event study" (2006), analyzing the stock market effects of privacy breaches.
• Garg, A.: Lead author of "Quantifying the financial impact of IT security breaches" (2003), examining different types of IT security breaches.
• Goldstein, J.: Lead author of "An event study analysis of the economic impact of IT operational risk and its subcategories" (2011), comparing function-related and data-related failures.
• Makridis, C. A.: Author of "Do data breaches damage reputation? Evidence from 45 companies between 2002 and 2018" (2021), quantifying reputational loss from breaches.
• Iyer, S. R.: Lead author of "Cyberattacks and impact on bond valuation" (2020), examining the effect of breaches on corporate bonds.
• Garg, P.: Author of "Cybersecurity breaches and cash holdings: Spillover effect" (2020), demonstrating how firms and their suppliers adjust cash holdings after breaches.
• Crosignani, M.: Lead author of "Pirates without borders: The propagation of cyberattacks through firms’ supply chains" (2023), studying the ripple effects of attacks like NotPetya.
• Kotidis, Antonis: Co-author of "Cyberattacks and Financial Stability: Evidence from a Natural Experiment" (2022), analyzing a multi-day cyberattack's impact on financial stability.
• Schreft, Stacey L.: Co-author with Kotidis on the financial stability effects of cyberattacks.
• Burgard, M.: Author of "Cyber Incident Response: The Real Cost of Not Having a Plan or Cyber Insurance" (2021), providing estimates for incident response costs.
• Bose, I.: Co-author of "The impact of adoption of identity theft countermeasures on firm value" (2013).
• Leung, A. C. M.: Co-author with Bose on identity theft countermeasures.
• Florackis, C.: Lead author of "Cybersecurity risk" (2023), showing how cybersecurity risk is priced in equity.
• Woods, D.W.: Co-author on several papers (2019, 2021) focusing on quantifying cyber harm estimates, effectiveness of security interventions, and the role of insurance.
• Böhme, R.: Co-author with Woods on quantifying cyber risk.
• Facchinetti, S.: Lead author of "Cyber risk measurement with ordinal data" (2020), suggesting alternative reporting methods for cyberattacks.
• Lhuissier S.: Co-author of "Measuring Cyber Risk" (2021), examining the correlation between cybercrime and cybersecurity company performance.
• Tripier, F.: Co-author with Lhuissier on measuring cyber risk.
• Bouveret, A.: Author of "Cyber risk for the financial sector: A framework for quantitative assessment" (2018), focusing on financial institutions.
• Keppo, Jussi: Co-author of "Do Hacker Groups Pose a Risk to Organizations? Study on Financial Institutions Targeted by Hacktivists" (2021).
• Niemela, Mikko: Co-author with Keppo on the risk posed by hacker groups.
• Anderson, R.: Co-author of "Measuring the cost of cybercrime" (2013), highlighting challenges in data collection.
• Chen, S.: Co-author on papers regarding challenges in cyber incident data.
• Kigerl, A.: Author of "Cyber Crime Nation Typologies: K-Means Clustering of Countries Based on Cyber Crime Rates" (2016), on data limitations in developing countries.
• Howell, C.J.: Co-author of "Datasets for analysis of cybercrime" (2020), discussing under-reporting issues.
• Burruss, G.W.: Co-author with Howell on cybercrime datasets.
• Altalhi, S.: Co-author of "A survey on predictions of cyber-attacks utilizing real-time twitter tracing recognition" (2021), on new data collection methods.
• Gutub, A.: Co-author with Altalhi on using social media for cyber incident identification.
• Harry, C.: Co-author of "Classifying cyber events" (2018), contributing to cybersecurity definitions.
• Gallagher, N.: Co-author with Harry on classifying cyber events.
• Ho, H.T.N.: Co-author of "Research trends in cybercrime victimization during 2010–2020: a bibliometric analysis" (2022), discussing varying definitions of cyber incidents.
• Luong, H.T.: Co-author with Ho on research trends in cybercrime.
• Aldasoro, I.: Co-author of "Operational and cyber risks in the financial sector" (2020), discussing cyber incidents as operational risk events.
• Biener, C.: Lead author of "Insurability of cyber risk: An empirical analysis" (2015), providing a comprehensive definition of a cyber incident.

Organizations/Entities:

• World Bank: Funded the research for "A Review of the Economic Costs of Cyber Incidents" through its Cybersecurity Multi-Donor Trust Fund.
• Bogazici University: Selcen Cakir's affiliation.
• PwC: Cited for reports stressing the importance of understanding economic costs of cyber incidents.
• U.S. CISA (Cybersecurity and Infrastructure Security Agency): Emphasizes the crucial role of understanding cyber incident economic costs for decision-making.
• IC3 (Internet Crime Complaint Center): Cited for their annual reports on internet crime, though their methodologies are noted as limited.
• Norton: Cited for their 2017 "Cyber Security Insights Report Global Results."
• McAfee & CISS (Center for Strategic and International Studies): Cited for their 2018 "Economic Impact of Cybercrime Report."
• eSentire & Cybersecurity Ventures: Provide annual cybercrime reports with significant projections for economic costs.
• European Commission: Cited for estimates on global annual costs of cyber incidents and cybercrime definitions.
• IBM: Cited for their "Cost of a Data Breach Report" (2022), providing detailed analysis of breach costs.
• ITU (International Telecommunication Union): Cited for its 2021 "Global Cybersecurity Index" and data on cybersecurity legislation across regions.
• CGTN: Cited for information on cybersecurity protocols in African businesses.
• Interpol: Cited for reports on African cyber threats and cost estimates for cybercrime in specific African countries.
• Serianu: Kenyan IT cybersecurity firm mentioned for its research on cybercrime's impact on Africa's GDP.
• UK Cabinet Office: Cited for its 2011 report on the cost of cybercrime in the UK.
• Grant Thornton: Cited for its 2021 report on the economic cost of cybercrime in Ireland.
• Accenture & Ponemon Institute: Cited for their 2019 "Securing the Digital Economy Report."
• Privacy Rights Clearinghouse (PRC): A key open-source database in the U.S. providing data on disclosed cyber breaches.
• FIRST: An organization mentioned in Goldstein et al. (2011) as a data source.
• ORX: A confidential consortium data source for operational risks in financial institutions.
• Cybereason: Cited for their 2022 "Ransomware: The True Cost to Businesses" report.
• NetDiligence: Cited for their 2022 "Cyber Claims Study."
• Kaspersky Lab: Cited for "IT Security Economics Report" (2021) and "Damage Control: The Cost of Security Breaches" (2017).
• Symantec: Cited for their 2019 "Internet Security Threat Report."
• Willis Towers Watson: Cited for their 2020 "Cyber Claims Analysis Report."
• CheckPoint – Software Technologies LTD: Cited for their 2021 "Cybersecurity Report."
• World Economic Forum (WEF): Cited for reports ranking cyber incidents as a top operational risk and discussing cybersecurity investments.

Enterprise Mobile app Architecture Specifics

Enterprise Mobile App Design Considerations:

Designing an enterprise mobile app architecture involves several key considerations. Here are the specifics of an end-to-end architecture with key points to keep in mind:

1. User Interface (UI):

   - User-Centered Design: 

Prioritize a user-friendly and intuitive interface to ensure user adoption and satisfaction.

   - Responsive Design:

Ensure the app works well on various device sizes and orientations.

2. Client-Side Components:

   - Mobile App Framework:

Choose a suitable framework or native development for the client-side.

   - Security:

Implement encryption, authentication, and secure data storage.

3. Backend Services:

   - Server Architecture: 

Decide between monolithic or microservices architecture based on scalability needs.

   - API Design: 

Develop well-documented RESTful APIs or GraphQL to facilitate communication with the app.

   - Authentication and Authorization:

 Implement robust user authentication and authorization mechanisms.

   - Data Storage:

Choose a database solution that aligns with your data requirements (SQL, NoSQL, or hybrid).

   - Caching:

Implement caching mechanisms to improve performance.

   - Scalability:

Design for scalability to handle increased loads as the user base grows.

4. Data Synchronization:

   - Ensure seamless synchronization between the mobile app and the server, even in offline mode.

   - Use data synchronization patterns like RESTful APIs, GraphQL, or WebSockets.

5. Security:

   - Implement strong security practices, including data encryption, secure APIs, and user authentication.

   - Regularly update the app to patch security vulnerabilities.

6. Quality Assurance (QA):

   - Develop a comprehensive testing strategy, including unit testing, integration testing, and usability testing.

   - Automate testing wherever possible to ensure consistency and reliability.

7. Deployment and Distribution:

   - Choose a deployment strategy (app stores, MDM solutions, or enterprise distribution) that suits your organization's needs.

   - Implement version control and continuous integration for streamlined updates.

8. Monitoring and Analytics:

   - Use tools for real-time monitoring to track app performance and user behavior.

   - Utilize analytics to gather insights and make data-driven improvements.

9. Updates and Maintenance:

   - Regularly release updates to improve functionality, security, and stability.

   - Provide customer support and a feedback mechanism for users.

10. Compliance and Regulations:

    - Ensure compliance with relevant data privacy regulations (e.g., GDPR, HIPAA) and industry-specific standards.

11. Cost Management:

    - Monitor and manage cloud service costs, as they can add up quickly.

12. Documentation:

    - Maintain comprehensive documentation for code, APIs, and architectural decisions.

13. Performance Optimization:

    - Continuously optimize app performance to reduce load times and enhance user experience.

14. User Support and Training:

    - Provide resources and training for users to effectively use the app.

15. Backup and Recovery:

    - Implement backup and recovery strategies for data and configurations.

16. Feedback Loops:

    - Establish mechanisms for gathering feedback from users and stakeholders to drive ongoing improvements.

17. Scalability and Growth:

    - Plan for future scalability and growth, considering the potential increase in users and data volume.

18. Comprehensive Testing:

    - Implement robust testing, including functional, security, and performance testing to identify and address issues.

19. Compliance and Security:

    - Ensure that the app complies with relevant regulations and security standards to protect sensitive data.

20. User Training and Support:

    - Offer user training and support channels to assist with onboarding and addressing user issues.

Remember that the specifics of your enterprise mobile app architecture may vary depending on your organization's unique requirements and constraints. Regularly review and adapt your architecture to meet evolving needs and technologies.

Business and Functional Skills

1) Business Skills:

Business skills in Information Technology (IT) refer to the competencies and knowledge required to effectively manage and operate IT-related activities within an organization. These skills may include project management, budgeting, strategic planning, and the ability to align IT solutions with the organization's goals and objectives.

Project Management: 

The ability to plan, execute, and oversee IT projects, ensuring they are completed on time and within budget.

Strategic Planning: 

Understanding how IT aligns with an organization's overall business strategy and goals.

Communication:

Effectively communicating technical information to non-technical stakeholders and clients.

Budgeting and Cost Management:

Managing IT budgets, controlling expenses, and making cost-effective technology decisions.

Vendor Management: 

The skill to negotiate and manage relationships with IT vendors and service providers.

Risk Management: 

Identifying and mitigating potential IT security and operational risks.

Legal and Compliance Knowledge:

Understanding data protection laws, regulations, and compliance requirements relevant to IT.

2) Functional Skills:

Functional skills in IT pertain to the technical abilities and expertise needed to perform specific tasks and functions within the IT field. These skills encompass areas such as programming, database management, network administration, cybersecurity, and software development. Functional IT skills are essential for executing the day-to-day technical operations in IT roles.

Programming and Coding: 

Proficiency in programming languages such as Java, Python, C++, etc.

Network Administration: 

Configuring and managing network infrastructure, routers, and switches.

Database Management: 

Expertise in designing, maintaining, and optimizing databases (e.g., SQL, NoSQL).

Cybersecurity: 

Knowledge of security protocols, encryption, and threat detection to protect data and systems.

System Administration: 

Managing and maintaining operating systems (e.g., Windows, Linux).

Cloud Computing: 

Skills in working with cloud platforms like AWS, Azure, or Google Cloud.

Web Development: 

Building and maintaining websites and web applications using technologies like HTML, CSS, and JavaScript.

DevOps Lead Interview Discussion Points between Human and ChatGPT

DevOps Lead Interview Questions (by human) and Responses Provided by ChatGPT. 

Human (prompt in http://chat.openai.com):

I want you to answer in a brief manner like an DevOps Lead with 10+ years of experience for below set of 7 interview questions.  Quote the question first, then answer for that question and also quote reference link from web for further reference per answer.

[1] How do you address/analyse the root cause of Out of Memory issues with  a Java based application?

2] Application load balancer Vs API Gateway - Explain the purpose of it and why we need both , can't we manage with one.?

[3] How to automate the collection of stack trace output in the event of application process crash or overload  ?

[4] What are the factors to consider when choosing between a Monolithic Vs Microservice application Architecture. Do we  really need go with microservices,  given it's complexity and additional cost involved?

[5] What are the tools for performing Static Code Analysis for improving code quality?

[6] What are the overhead/complications with managing containers?

[7] Consider there is a production release in 1-2 days and assuming developers usually commit their code at the last moment, how do you design a CI/CD pipeline for an environment with 100+ PRs in parallel from Developers for multiple features and ensure all the features are committed  and delivered to customer?

ChatGPT: 

Certainly, I can provide detailed responses to each of these DevOps interview questions.

[1] How do you address/analyse the root cause of Out of Memory issues with a Java-based application?

Identifying and addressing Out of Memory issues in a Java-based application involves several steps:

1. Monitoring and Alerts: Set up monitoring tools like Prometheus, Grafana, or APM solutions (e.g., New Relic, AppDynamics) to track JVM memory usage and trigger alerts when memory thresholds are exceeded.

2. Heap Dump Analysis: When an Out of Memory error occurs, generate a heap dump using tools like `jmap` or `-XX:+HeapDumpOnOutOfMemoryError` JVM option. Analyze the heap dump with tools like Eclipse MAT (Memory Analyzer Tool) to find memory leaks and memory-hungry objects.

3. Thread Dump Analysis: Concurrent issues can also cause memory problems. Capture and analyze thread dumps using tools like `jstack` to identify blocked or deadlocked threads.

4. Code Review: Review the application code for memory-efficient practices such as proper object lifecycle management, avoiding memory leaks, and optimizing data structures.

5. Tuning JVM Parameters: Adjust JVM parameters (heap size, garbage collection strategy) based on the application's memory requirements.

6. Profiling: Use profiling tools like YourKit or VisualVM to identify memory-hungry methods or classes.

[2] Application load balancer Vs API Gateway - Explain the purpose of it and why we need both, can't we manage with one?

- Load Balancer: Load balancers distribute incoming traffic across multiple application instances or servers, ensuring high availability and scalability. They operate at the transport layer (L4) and are typically used for routing TCP/UDP traffic.

- API Gateway: API Gateways operate at the application layer (L7) and are designed for managing API traffic, including authentication, authorization, request/response transformation, and rate limiting.

While some tasks overlap, they serve different purposes. You might need both because:

- Load balancers handle basic traffic distribution and failover, but lack API-specific features.

- API Gateways provide advanced API management features, including security and request/response modification, but don't manage raw network traffic or server health.

Using both ensures a robust architecture that balances network-level needs with API-specific requirements.

[3] How to automate the collection of stack trace output in the event of application process crash or overload?

Automating stack trace collection in the event of a crash or overload can be achieved with tools like:

- Process Monitors: Use tools like systemd (on Linux) or Windows Service Manager (on Windows) to monitor your application processes. Configure them to restart the process upon failure and collect error logs.

- Crash Dumps: Configure your application to generate crash dumps upon failure. Tools like `coredumpctl` (Linux) or Windows Error Reporting (WER) can collect these dumps.

- Log Aggregation: Send logs to centralized logging systems like ELK Stack, Splunk, or Graylog. Create alerts for specific log patterns indicating crashes or overloads.

- Application Performance Monitoring (APM): APM tools like New Relic or AppDynamics can automatically capture stack traces during performance anomalies.

Implementing a combination of these approaches ensures automated stack trace collection in critical scenarios.

[4] What are the factors to consider when choosing between a Monolithic Vs Microservice application Architecture? Do we really need to go with microservices, given its complexity and additional cost involved?

Choosing between Monolithic and Microservices architecture depends on factors like scalability, complexity, and team size:

- Monolithic: Simplicity, easier development, and deployment for small teams or simple applications. It can be cost-effective initially.

- Microservices: Scalability, agility, and the ability to use different technologies for each service. Suitable for complex, rapidly evolving projects but can be costlier to manage.

Consider factors like project size, team expertise, scalability requirements, and long-term maintenance costs. Microservices are beneficial for large, complex systems with high scalability demands but can introduce complexity that smaller projects might not need.

[5] What are the tools for performing Static Code Analysis for improving code quality?

There are several tools for Static Code Analysis:

- SonarQube: A comprehensive platform for code quality and security analysis.

- Checkmarx: Specializes in application security testing (SAST).

- PMD: Focuses on code style and potential issues.

- ESLint (JavaScript/TypeScript): Lints and finds code quality issues.

- FindBugs (Java): Identifies bugs in Java code.

- RuboCop (Ruby): A Ruby static code analyzer and formatter.

- Pylint (Python): A Python static code analysis tool.

- TSLint (TypeScript): Linter for TypeScript code.

Choosing the right tool depends on your programming language and specific code quality requirements.

[6] What are the overhead/complications with managing containers?

Managing containers introduces challenges:

- Complexity: Orchestrating containers with tools like Kubernetes can be complex.

- Resource Overhead: Containers consume system resources for orchestration and isolation.

- Security: Ensuring container security (e.g., image vulnerabilities, runtime security) is critical.

- Networking: Configuring container networking and service discovery can be complex.

Mitigate these challenges with proper planning, automation, and monitoring.

[7] Consider there is a production release in 1-2 days and assuming developers usually commit their code at the last moment, how do you design a CI/CD pipeline for an environment with 100+ PRs in parallel from Developers for multiple features and ensure all the features are committed and delivered to the customer?

Designing a robust CI/CD pipeline for such a scenario involves:

- Parallel Testing: Implement parallel test execution to speed up testing of multiple PRs concurrently.

- Automated Regression Tests: Ensure a comprehensive suite of automated regression tests.

- Feature Flags: Use feature flags to enable/disable specific features, allowing you to release them independently.

- Code Freeze: Implement a code freeze period before the release to stabilize the codebase.

- Rolling Deployments: Use blue-green or canary deployments to minimize downtime during the release.

- Monitoring and Rollback: Implement real-time monitoring and automated rollback mechanisms in case of issues.

By combining these strategies, you can manage a high volume of PRs and ensure a smooth production release.

Cyber security Theory

 OSI Layers - Open Systems Interconnection Reference Model

1. Application  - Provides application connection HTTP, FTP, SMTP, SNMP etc., 
2. Presentation - Data formatting  and encryption takes place - known as syntax layer - ASCII encoded to EBCDIC Or vice-versa
3. Session
4. Transport -  connects low level communication hardware in level 1 through 3 with higher level software in levels 5 through 7 - TCP, UDP - Communicates in segments
5. Network - Sends and receives in Packets (Chunk of data) 
6. Datalink

Sends and receives frames (a sequence of 64 to 1518 bytes). Two sub-layers:-  

1. MAC - Media Access Control

2. LLC - Logical link Control (Used by network switches)

    7. Physical - Data transmitted in bits. Cable and radio spectrum

Hint:-  Please Do Not Tell Sales People Anything to remember OSI layers

Network topology

1) Bus - No central device - only T connector 

2) Ring - No central device - only T connector 

3) Star - Central device connects each device

4) Mesh - Every device connects to each other

5) Wireless Mesh

Hybrid Topology

2 or more network topology

Point to point communication

Point to multi-point

CIA - Confidentiality Integrity Availability

Risk Identification

Risk Assessment

Quantitative Risk Analysis Vs Qualitative Risk Analysis

Asset Value

Exposure Factor

Single Loss Expectancy = Asset Value (AV) x Exposure Factor (EF)

Annualized Rate Of Occurrence

ALE = SLE x ARO

CVSS (Common Vulnerability Scoring System) Calculator

https://first.org

Risk Response (Take Decision) - Evaluating Counter measures - Provide a proposal - Adjusting the finding 

Risk Treatment - Avoid (You can do but are not doing - Ignore) / Accept (You cannot do anything - Accept the existence of vulnerability and move on) / Mitigate (Fix the vulnerability) / Transfer (Risk to the supplier, eg: Cloud Provider to take care)

Goal of Risk Management - Minimise the risk in the org and maintain the same

Residual Risk - Risk which remains after counter-measures (Never Eliminated but lesser in-nature)

Risk Monitor - Contiuous monitoring of the risk - Qualsys , Nessus

Key Performance Indicator (KPI) - How well a process is progressing towards a intends goal

Key Risk Indicator (KRI) - Metrics , which defines risk threshold of an organisation - how many denial of service (DoS) - How many acceptable and not acceptable - eg:- Quantity of unauthorized software , no-antivirus

KPI and KRI are SMART metrics

KPI - underperforming KRI - Early Warning

SMART - Specific Measurable  Attainable Relevant Time-Bound 

Security Controls: - Policies to protect CIA of the asset - Administrative Controls - no impact to work , security training - Technical Controls - Firewall in-place , Physical Controls - CCTV 

Control Types - Detective (sensor) , Preventive (lock) , Deterrant (violating company policies) , Corrective , Recovery Control (Backup) , Compensating (Alternative - Intent , similar level of defense)

Control Selection - High Security in Jewellery Shop , Bank. College - Normal Security

Risk Management Frameworks: NIST , CIA

NIST - National Institute Of Science and Technology (United States Federal Govt) 

1. Prepare
2. Categorize
3. Select
4. Implement
5. Assess
6. Authorize
7. Monitor

NICE - National Initiative for Cybersecurity Education

1. Identify
2. Protect
3. Detect Respond 
4. Recover