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.
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.
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 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.
Enterprise data is susceptible to various risks that can compromise its confidentiality, integrity, and availability. Here are the key risks associated with enterprise data:
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
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.
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.
Regulatory compliance has a significant impact on enterprise data protection. Here are some primary ways in which regulatory compliance influences data protection practices:
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.
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.
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.