Enterprise Data Protection - Guide and Solutions

‍What is Enterprise Data?

Enterprise data is a tremendous asset, but did you know that it could also be a cause of great data privacy-related financial risks? The need for sturdy enterprise data protection cannot be emphasized enough. With local data privacy laws such as GDPR being strictly enforced by countries around the world, companies are seeing heftier fines for data breaches.  

• Fines issued for GDPR non-compliance increased sevenfold, from $180 million in 2020 to under $1.25 billion in 2021 (Source: Securityweek).  
• According to the third 2021 Insider Data breach survey commissioned by Egress, 94 percent of organizations have had a data breach in the last 12 months.  
• According to IBM’s annual Cost of a Data Breach Report, the average cost of a data breach has increased from $3.86 million in 2020 to $4.35 million in 2022. In the US, the cost is more than doubled at USD $9.45 million.  

Companies now need to be extremely cautious about how they manage privacy risks by carefully controlling access to personal and sensitive data.  

With enterprise data growing at a fast clip, and storage getting cheaper, organizations are accumulating and storing terabytes of customer and employee data and moving much of the data to the cloud and accessing it as needed on a real-time basis for things such as Analytics and AI-driven insights. The more the data, the greater the risk. But what are companies doing about the inherent risks and costs associated with managing that data? The solution lies in deploying solid enterprise data protection solutions.

When you look at companies like Google, Microsoft, Apple and many other Multi-National Companies (MNCs), they spend billions of dollars solely to advertise their product. Such companies usually make informed decisions from the data they possess which has been collected, curated and customized over long periods of time. 

These enterprise data are rich with user’s personal information and lifestyle based on which they can find the perfect way to focus on the target consumers who will garner them a lot of profit. Since these data points are so essential for these companies, they are also a ripe target for malicious users looking for a quick profit.

As a result, there have been various measures devised to protect sensitive PII (Personally Identifiable Information) from the Enterprise data. Enterprise Data Protection measures have been underway for quite a while.

What is Enterprise Data Protection?

Cybersecurity measures are implemented to ensure Enterprise Data Protection on a large scale. With the introduction of Generative AI and Public LLMs like ChatGPT, Gemini, Perplexity and so on, the bar of entry for malicious users to be able to initiate cybersecurity attacks has lowered. This, in turn, has increased the amount of cybersecurity attacks on various enterprises for various reasons.

The implementation of various strategies such as data tokenization, encryption decryption and granular access are some of the common ways enterprises protect their data.

Protecto provides all these protections with their revolutionary Intelligent Data Tokenization using their private LLM and also provides an agentless solution since most enterprise data are stored in the cloud.

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So how can you protect your enterprise data? Do you introduce more security clearances, and tune your LLM to detect vulnerabilities? How can you ensure the best Enterprise Data Protection that can be provided?

Enterprise Data Protection Strategy

There are many ways to protect your enterprise data. This includes feeding the LLMs only anonymised data so that the Model will not remember the users when queried. This makes them resistant to Prompt Injections. By implementing Retrieval Augmented Generation (RAG), you can update your LLM protecting them from hallucinations.

Protecto hosts their private SaaS server from which they will use their LLM to pseudonymise your data. By tokenizing your data, they will store these token maps in a secure token vault which can only be accessed by them. This ensures the highest level of data security.

Protecting sensitive data is a paramount concern for any organization in today's digital landscape. A robust Data Protection Strategy is essential to safeguard information and mitigate risks. Drawing inspiration from competitor content, we can tailor our approach to ensure a comprehensive and effective data protection framework. Here's a breakdown of our strategy:

Comprehensive Audit of Sensitive Data:

Conducting a thorough audit is the foundation of our strategy. We will identify and classify all sensitive data within our organization, including personal, financial, and proprietary information. This audit will serve as a baseline to understand the scope of our data protection efforts.

Holistic Risk Assessment:

We will go beyond internal risks and assess both internal and external threats to our data security. This involves identifying vulnerabilities within our systems, evaluating potential cyber threats, and understanding the risks associated with third-party collaborations. A comprehensive risk assessment will guide our strategy to proactively address potential pitfalls.

Tailored Data Protection Policy:

Building on the audit and risk assessment, we will define a Data Protection Policy tailored to our organization's unique needs. This policy will outline guidelines for data handling, access controls, encryption, and incident response. It will serve as a roadmap for employees to follow in order to maintain the highest standards of data protection.

Integrated Security Strategy:

Our security strategy will encompass both technical and procedural measures. This includes implementing state-of-the-art cybersecurity solutions, regular security updates, access controls, and employee training programs. By integrating these elements, we aim to create a robust defense against potential breaches.

Proactive Compliance Strategy:

Staying abreast of regulatory requirements is critical. Our strategy will involve ongoing monitoring and adjustment to ensure compliance with data protection laws and industry standards. This proactive approach will not only safeguard our data but also build trust with clients and stakeholders.

Continuous Improvement and Adaptation:

The digital landscape is dynamic, and threats are ever-evolving. Therefore, our strategy includes a commitment to continuous improvement. Regularly revisiting and updating our data protection measures will allow us to adapt to emerging risks and technologies, ensuring the long-term resilience of our data protection framework.

By combining these elements into a cohesive Data Protection Strategy, we aim to create a secure and resilient environment for our sensitive data, instilling confidence among our stakeholders and mitigating potential risks effectively.

The Importance of Enterprise Data Protection

The primary goal of Enterprise Data Protection is to establish a robust security framework that mitigates the risks associated with data breaches, cyberattacks, and other potential threats. This involves implementing a multi-layered approach to protect data at various stages of its lifecycle, from creation and storage to transmission and disposal.  

A comprehensive approach to enterprise and AI data protection helps safeguard sensitive information, maintain regulatory compliance, preserve customer trust, and mitigate the potential damages associated with data-related risks.

By prioritizing enterprise data protection, organizations can safeguard their reputation, maintain business continuity, avoid legal consequences, and protect valuable assets such as intellectual property and customer data. It also enables organizations to demonstrate their commitment to data privacy, enhance stakeholder relationships, and gain a competitive edge in an increasingly data-driven world.

Types of Enterprise Data Risks  

Enterprise data is susceptible to various risks that can compromise its confidentiality, integrity, and availability. Here are the key risks associated with enterprise data:

• Data Privacy Risks:  
  Data privacy risks involve unauthorized access, use, or disclosure of sensitive information. This can lead to violations of privacy regulations, damage to individual privacy rights, reputational harm, and legal consequences. Mishandling personal identifiable information (PII) or sensitive customer data can result in loss of trust, customer attrition, and potential lawsuits.
  
• Data Security Risks: Data security risks encompass threats to the confidentiality, integrity, and availability of data. These risks include cyberattacks, data breaches, malware infections, insider threats, and physical theft. A successful security breach can result in financial losses, intellectual property theft, disruption of operations, and damage to the organization's reputation.
  
• Compliance Risks: Compliance risks arise from the failure to adhere to applicable laws, regulations, and industry standards. Organizations operating in regulated sectors, such as finance, healthcare, or telecommunications, face specific compliance requirements related to enterprise data protection, privacy, retention, and reporting. Non-compliance can result in substantial fines, legal penalties, and reputational damage.
  

To mitigate these risks, organizations must prioritize robust enterprise data protection measures, including implementing strong security controls, encrypting sensitive data, conducting regular risk assessments, establishing data governance frameworks, and ensuring compliance with relevant regulations.  

Interesting read: How to Conduct an Effective Data Privacy Risk Assessment

3 Steps to Protecting Enterprise Data

Consider for a moment that an enterprise has decided to store its data consisting of 40 billion rows on the Snowflake data cloud (the data could be stored on any other cloud, but the risk remains the same). To understand the overall risks and costs associated with this data, we must understand the size of the risk and then determine the impact by factoring in the probability of a breach event. So, let us go ahead and do this analysis in a stepwise manner.

Step 1. Understanding how much risk does $1 of storage hold

First, we need to understand the size of the data privacy risk based on the extent of enterprise data. The size of the risk is directly proportional to the amount of enterprise data held by an organization.  

Assuming that a company is based in the United States, Snowflake storage costs¹ begin at a flat rate of USD $23 per compressed TB of data stored per month, which translates to USD $276/TB/year. Based on our experience and analysis, a 1 TB of Snowflake database will have roughly 6.1 billion rows. So, if a company spent $1 on storage, it could store 22.1 million rows – see table 1 below for the computation.

Table 1: Number of Rows per $1 in storage costs in Snowflake

Based on our experience, we have observed that roughly 3-5% of the total enterprise data is personal (PI) data and about 1% of data is highly sensitive Personal Identifiable Information (PII) data. Now, let us make a conservative assumption that this 1TB of data contains 1% of Personal Information (PI) and 0.1% of Personal Identifiable Information (PII). This equates to 220k rows of PI and 22k rows of PII.  

¹ How Usage-Based Pricing Delivers a Budget-Friendly Cloud Data Warehouse

Step 2. Assessing the size of the risk ($1 in storage = $247k in risk)

Next, let us assess the data privacy risk of the risk for 1TB of data. Risk estimates depend on two factors – the size of the impact of an incident and the likelihood of occurrence of an incident.

Recent IBM breach report studies show that 1 record costs $181 in breach-related costs. So, a 1TB database having 220k rows of PI and 22k rows of PII would translate to roughly $5.46 million in breach-related costs (contact us to find out more about our calculation methodology).  

The next question is, what is the likelihood of the occurrence of a breach? Various studies and research put these numbers in a wide range. For this analysis, we used the Journal of Cybersecurity’s 2016 report estimate of a 4.5% chance of a breach of such magnitude could happen in an organization.

Using the above framework of the size of impact times likelihood, we calculate the total risk to be approx. USD $247k from the data contained in storage that costs $1.

Step 3. Calculating compliance and data protection overhead cost ($1 in storage = $100+ in data protection overhead)

Finally, let us consider a scenario where a company sitting with 40 billion rows of enterprise data is worried about meeting compliance, getting hacked, or being penalized for privacy violations. This is a real-life scenario of a USD $70 billion Asset Management company that approached us when it took stock of its data governance, compliance, and data privacy risks.  

To calculate the cost associated with the process of determining the effort needed to perform a risk assessment, the company would traditionally need to hire a team of data engineers and commence a data audit process that would take 8-9 months. We used the estimates provided by this company and assuming an average salary of $200K per engineer, the calculations led to a total cost of USD $180k to perform a data audit and compliance of 40 billion rows (or about $100 for every $1 of storage).

Table 2: Effort to perform data compliance audit assessment with a team of engineers (estimates provided by the company's internal team)

Considering that this process needs to be repeated every quarter, the company would be looking at an annual cost of performing compliance assessment at USD $720k. (Note: in this analysis we are not factoring in the data stored in additional instances such as test and sandbox instances, which would also hold personal data – so in total likelihood the total cost could well exceed USD 1 Million for this company, notwithstanding the additional time and effort).  

With the company’s enterprise data constantly expanding, the above audit process must be repeated periodically to adhere to compliance requirements, as determined by the company’s policy and local laws – so the costs and effort will increase over time. It is obvious that this process is cumbersome, time-consuming, and not scalable.

Also read: Impact Of Regulatory Compliance Laws On Data Privacy & Security

Impact of Regulatory Compliance on Enterprise Data Protection

Regulatory compliance has a significant impact on enterprise data protection. Here are some primary ways in which regulatory compliance influences data protection practices:

• Data Security Requirements: Regulatory frameworks, such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and Health Insurance Portability and Accountability Act (HIPAA), impose specific data security requirements on organizations. Compliance with these regulations necessitates implementing appropriate data protection measures, including data masking, access controls, data classification, and incident response protocols.
  
• Data Breach Notification: Many regulatory frameworks mandate organizations to promptly notify individuals and relevant authorities in the event of a data breach, which often leads to loss of brand reputation and confidence. This requirement is the main driver for organizations to establish robust data protection mechanisms and incident response plans to minimize the impact of breaches and ensure timely notification.
  
• Privacy Rights and Consent Management: Regulations often emphasize individuals' privacy rights and their control over their personal data. Compliance entails implementing measures to obtain and manage explicit consent, providing transparency regarding data collection and usage, and empowering individuals to exercise their rights, such as data access, correction, and deletion.
  

• Data Retention and Disposal: Regulatory compliance guidelines often dictate specific data retention periods and disposal requirements. Organizations must establish data retention policies that align with these regulations and ensure secure disposal of data when it is no longer needed.
  
• Vendor and Third-Party Management: Regulatory compliance extends to data protection practices of vendors and third-party service providers. Organizations are responsible for ensuring that their partners adhere to enterprise data protection standards, which often involves conducting due diligence assessments, contractually binding them to comply with relevant regulations, and monitoring their data handling practices.
  
• Compliance Audits and Penalties: Regulatory bodies may conduct audits to assess organizations' compliance with data protection requirements. Non-compliance can result in severe penalties, fines, and reputational damage. To mitigate these risks, organizations must establish robust data protection measures and maintain proper documentation to demonstrate compliance during audits.
  
• International Data Transfers: Regulations like GDPR impose restrictions on the transfer of personal data outside of specific regions or countries that may not provide an adequate level of enterprise data protection. Compliance necessitates implementing appropriate safeguards, such as Standard Contractual Clauses (SCCs) or adopting Privacy Shield frameworks, to ensure lawful and secure international data transfers.
  

Organizations must understand and adhere to relevant regulations, establish robust data protection frameworks, regularly assess their compliance, and adapt their practices to evolving regulatory requirements.  

How Protecto Can Enterprise Data Protection Solutions

Companies are facing the growing challenge of having to carefully manage sensitive and personal data. It is evident from the above analysis that companies are sitting on a minefield of risk associated with their enterprise data protection. The specific customer example above illustrates the extent of the risk from a data privacy risk perspective as well as highlights the time, effort, resources, and cost needed if a company were to manage the risks.

The strategy of employing a team of engineers is simply not a scalable approach since companies need to assess risks instantly, as opposed to taking months to do a risk assessment and then repeating the process periodically. Protecto recognizes this pain and offers a solution that provides instantaneous, and continuous insights into where the risks prevail.  

In addition, Protecto’s quick discovery of risks will enable companies to undertake expeditious responses to address those risks thereby preventing companies from having to pay costly fines. As a bonus, companies can also save costs by eliminating the need to hire a team of data engineers to perform this tedious task. Moreover, the real-time insights will also accelerate compliance reporting.  

Do you know the extent of your data privacy risks? Do you have a way to ascertain this quickly? If you are unable to answer the above and are interested in doing a personalized risk assessment of your enterprise data, irrespective of which cloud data storage vendor you use, contact us today for a free risk assessment. You can even schedule a demo to discuss how Protecto can help you uncover your data privacy risks and protect your sensitive data.  

Frequently asked questions on Enterprise Data Protection

What does enterprise data security mean?
Enterprise data security encompasses the comprehensive practices of implementing, monitoring, and overseeing security measures across all data objects and repositories within an organization. This wide-ranging approach involves various tools, techniques, policies, and frameworks to guarantee the security of data, regardless of its storage location or usage within the organizational structure.

How does data tokenization help in enterprise data protection?

Tokenizing data offers a strategic approach to reduce the exposure of sensitive information by limiting its storage locations. By assigning tokens to users and applications that require data analysis, access to the original sensitive data is minimized. This token-based approach enables multiple applications and processes to interact with the token data while preserving the security of the underlying sensitive information.

Why is enterprise data protection important?

Enterprise data protection is essential because it safeguards sensitive information, such as customer data, financial records, and intellectual property, from unauthorized access, theft, or misuse. It helps maintain the trust of customers, partners, and stakeholders while preventing costly data breaches.

What are the primary threats that enterprise data protection aims to mitigate?  

Enterprise data protection addresses a range of threats, including cyberattacks (e.g., ransomware, phishing), insider threats, accidental data exposure, data leakage, and unauthorized access attempts.

How does enterprise data protection impact regulatory compliance?

Adequate enterprise data protection is often a requirement in data protection regulations, such as GDPR, HIPAA, or CCPA. Complying with these regulations is crucial for avoiding legal penalties and maintaining the trust of customers who value their privacy.

How does enterprise data protection contribute to business continuity?  

Data protection ensures the availability and integrity of critical data, which is vital for business continuity during unforeseen events like natural disasters, cyber incidents, or hardware failures.

How can an enterprise build a robust data protection strategy?

Building a robust data protection strategy involves conducting a comprehensive risk assessment, identifying critical assets and data, implementing appropriate security measures, educating employees, and regularly evaluating and enhancing the security posture based on industry best practices.