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Cyber Insurance In India

With the rise of digital technologies and platforms, cyber threats such as data breaches, social media scams, and ransomware have increased. In India, CPR reported an 18% increase in weekly cyber attacks in Q1, ’23. 

In such tumultuous times, cyber insurance has become important to mitigate these risks and protect themselves against potential losses.

What is Cyber Insurance? 

Cyber insurance is a policy designed to protect individuals and businesses against losses from cyber attacks or data breaches. They usually cover a range of costs associated with a cyber attack, including investigating the attack, restoring lost data, and providing notification to affected parties. 

Why does India need to adopt Cyber Insurance?

In India, cyber threats loom large, with each organization facing an average of 2100 attacks weekly in 2023.   

The threats have risen in recent years, driven by the increasing use of digital technologies, a growing number of internet users, and a lack of cybersecurity awareness. 

Here are some of the most common cyber threats faced by individuals and businesses in India:

  1. Malware: Malware is a software designed to harm computer systems or steal sensitive information. 
  2. Ransomware: Ransomware is malware that locks down a victim’s computer or files and demands payment for the data release.
  3. Phishing: Phishing attacks involve using fraudulent emails, text messages, or websites to trick users into giving away PII such as passwords or credit card numbers. 
  4. Social engineering: Social engineering attacks involve manipulating human behavior to gain access to sensitive information or computer systems. Examples include pretexting, baiting, and quid pro quo attacks. 
  5. Cyber espionage: The use of hacking techniques to steal sensitive information from government organizations, businesses, or individuals. 

What are the various types of cyber insurance available in India?

Cyber insurance is still a relatively new concept in India, and as of now, the penetration of cyber insurance in India is low. However, there is a growing awareness of insurance in organizations. According to a recent report by PwC India and the Data Security Council of India (DSCI), India’s cyber insurance market is expected to grow at a CAGR of 35% from 2021 to 2025. 

Cyber insurance policies are classified into various types as per the coverage they provide: 

A. Data breach coverage – Data breach coverage can help cover the costs associated with investigating the breach, notifying affected parties, providing credit monitoring services, and restoring lost data.

In India, ICICI Lombard is a prominent company covering this breach and business interruption coverage. 

B. Cyber extortion coverage – Cyber extortion is an attack where attacker threatens to harm an individual or business unless the ransom is paid. In these attacks, the attacker may threaten to release sensitive information, disable computer systems, or launch a distributed denial of service (DDoS) attack.

One example of an Indian insurance company that provides cyber extortion coverage is HDFC ERGO. Their cyber insurance policy covers losses resulting from cyber extortion, including the costs associated with ransom payments, hiring a security consultant, and crisis management expenses.

C. Business interruption coverage – Business interruption coverage can provide financial assistance to businesses that experience a cyber attack causing their systems to go offline and preventing normal business operations.

Other common insurances include Liability coverage, Crisis management coverage, Legal coverage, and Social engineering fraud coverage. 

Cyber Insurance Market in India 

Globally, the cyber insurance market is expected to grow at a CAGR of 27% from 4.2 billion USD to 22.8 billion USD from 2017 to 2024. In India, it remains at a nascent stage. However, with growing awareness, the penetration has seen a substantial Y-o-Y increase. 

As Mantra Labs recently worked with India’s largest private insurance company to improve their cyber insurance journey, we understood that the key focus was to be on ensuring customers understand the risks involved and the impact of various benefits/add-ons provided. 

In order to improve the offtake, insurers need to focus on customers’ digital experience while selecting an insurance plan. 

Some of the prominent insurance companies offering cyber insurance include – 

  1. HDFC Ergo
  2. Bajaj Allianz
  3. ICICI Lombard
  4. Tata AIG
  5. Reliance General

How to Select the Right Cyber Insurance Policy in India 

Choosing the right cyber insurance policy is a key decision for businesses in India. Here are some factors to keep in mind:

  1. Coverage: Businesses should look for a policy that covers a range of cyber risks, including data breaches, cyber extortion, and business interruption.
  2. Policy limits: It’s essential to understand the limits of your cyber insurance policy, including the amount of coverage it provides and any deductibles or exclusions that may apply. 
  3. Cost: Cyber insurance policies can vary widely in price, so it’s critical to consider the cost of the policy to the coverage it provides. Look for a policy that offers good value for the cost.
  4. Reputation: When choosing a cyber insurance policy, it’s essential to consider the insurance provider’s reputation. Companies should prefer a credible insurer with a good customer service team.
  5. Risk management services: Many cyber insurance policies come with risk management services and resources that can help businesses identify and mitigate cyber risks. Look for a policy that includes these types of services.
  6. Claims process: Finally, it’s key to understand the claims process for your cyber insurance policy. 

Choosing the right cyber insurance policy requires careful consideration of these factors to ensure your business is adequately protected against the growing threat of cyber attacks.

India accounts for just 5% of the global cyber insurance market. However, the future is promising.

As the market for cyber insurance in India grows, we expect to see more innovative policies and risk management services to help businesses prevent and respond to cyber incidents.

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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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