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The ‘Cyber Attacks’ Winter is Coming — straight for small firms in India Inc.

5 minutes read

Cyber intrusions and attacks have increased exponentially over the last decade approximately, exposing sensitive information pertaining to people and businesses, thus disrupting critical operations, and imposing huge liabilities on the economy. 

Cybersecurity is a responsibility that employees and leaders across functions must shoulder simply because it is the gospel truth – you cannot protect what you cannot see. As organizations have shifted to the work-from-home model due to the outbreak of the COVID-19 pandemic, it’s increasingly important to keep your company’s data secure. 

While the pandemic has led to near or complete digitalization of operations amongst financial institutions, it’s also increased the potential for cyberattacks that lead to adverse financial, reputational, and/or regulatory implications for organizations. 

According to Accenture, cybercrime is said to cost businesses $5.2 trillion worldwide within five years. “With 43% of online attacks now aimed at small businesses, a favorite target of high-tech villains, yet only 14% prepared to defend themselves, owners increasingly need to start making high-tech security a top priority,” the report continues.

A recent McAfee study shows global cybercrime costs crossed US$1 trillion dollars in 2020, up almost 50% from 2018.

India too saw an exponential rise in cybersecurity incidents amid the coronavirus pandemic. Information tracked by the Indian Computer Emergency Response Team (CERT-In) showed that cybersecurity attacks saw a four-fold jump in 2018, and recorded an 89 percent growth in 2019.

The government has set up a Cyber Crisis Management Plan for countering cyber-attacks effectively, while also operating the Cyber Swachhta Kendra (Botnet Cleaning and Malware Analysis Centre)

Banks and Financial Institutions (FIs) are some of the highest targeted market sectors. An analysis by FitchRatings in collaboration with SecurityScorecard reveals that banks with higher credit ratings exhibited better cybersecurity scores than banks with lower credit ratings. 

Bharti Airtel’s chief executive officer for India, Gopal Vittal, in a letter to the telco’s 307.9 million subscribers, detailed out how Airtel is carrying out home delivery of SIM cards and cautioned subscribers from falling prey to cyber frauds. He cautioned them against the rapid rise in cyber frauds, highly likely via digital payments. “There has been a massive increase in cyber frauds. And as usual, fraudsters are always finding new ways to trick you,” he added in the letter. 

Barcelona-based Glovo, valued at over $1 billion, that delivers everything from food to household supplies to some 10 million users across 20 countries, came under attack recently when the “hacker gained access to a system on April 29 via an old administrator platform but was ejected as soon as the intrusion was detected”, according to the company.

The attack came less than a month after Glovo raised 450 million euros ($541 million) in funding. 

According to Kaspersky’s telemetry, close on the heels of coronavirus-led pandemic and subsequent lockdown in March 2020, saw a total number of meticulously planned attacks against remote desktop protocol (RDP) jumped from 93.1 million worldwide in February 2020 to 277.4 million 2020 in March — a whopping 197 percent increase. In India, the numbers went from 1.3 million in February 2020 to 3.3 million in March 2020. In July 2020, India recorded its highest number of cyberattacks at 4.5 million.

The recent data breach at the payment firm Mobikwik, affected 3.5 million users, exposing Know Your Customer (KYC) documents such as addresses, phone numbers, Aadhaar card details, PAN card numbers, and so on. The company, however, still maintains that there was no such data breach. It was only after the Reserve Bank of India’s intervention that Mobikwik got a forensic audit conducted immediately by a CERT-IN empaneled auditor and submitted the report. 

Security experts have observed a 500% rise in the number of cyber attacks and security breaches and a 3 to 4 times rise in the number of phishing attacks from March until June 2020.

These attacks, however, are not just pertaining to the BFSI sector, but also the healthcare sector, and the education sector.

Image Source: BusinessStandard.com

What motivates hackers to target SMBs? 

Hackers essentially target SMBs because it’s a source of easy money. From inadequate cyber defenses to lower budgets and/or resources, smaller businesses often lack strong security policies, cybersecurity education programs, and more, making them soft targets. 

SMBs can also be a ‘gateway’ to larger organizations. As many SMBs are usually connected electronically to the IT systems of larger partner organizations, it becomes an inroad to the bigger organizations and their data. 

How can companies shield themselves from a potential cyberattack: 

As a response to the rising number of attacks in cyberspace, the Home Ministry of India issued an advisory with suggestions on the prevention of cyber thefts, especially for the large number of people working from home. Organizations and key decision-makers in a company can also create an effective cybersecurity strategy that’s flexible for adaptation in a changing climate too. Here are a few use cases: 

  • CERT-In conducted ‘Black Swan – Cyber Security Breach Tabletop Exercise’, in order to deal with cyber crisis and incidents emerging amid the COVID-19 pandemic, resulting from lowered security controls. 
  • To counter fraudulent behavior in the finance sector, the government is also considering setting up a Computer Emergency Response Team for the Financial Sector or CERT-Fin.
  • Several tech companies have come forth to address cybersecurity threats by building secure systems and software to mitigate issues like these in the foreseeable future. For example, IBM Security has collaborated with HCL Technologies to streamline threat management for clients through a modernized security operation center (SOC) platform called HCL’s Cybersecurity Fusion Centres. 

Some of the ways through which companies can mitigate potential risks include: 

  • Informing users of hacker tactics and possible attacks
  • Establish security rules, create policies, and an incident response plan to cover the entire gamut of their operations
  • Basic security measures such as regularly updating applications and systems
  • Following a two-factor authentication method for accounts and more

While these measures are some of the ways to be on top of your game in the cybersecurity space, they will also help in sound threat detection while helping gain better insights into attacks and prioritizing security alerts so that India is better prepared for an oncoming attack and battling any unforeseen circumstance that might result in huge loss of data, resources and more. 

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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