A Guide to Sensitive Data Protection
Harsh Sahu
CTO
February 2024 | 12 mins
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Sensitive data, as the name suggests, are those data that can be used to identify an individual. Such data are distributed across platforms such as cloud environments, data lakes, marketing channels, training data in LLMs, etc.
With the rise in sensitive personal data collection and distribution, there are many concerns related to sensitive data breach, privacy violation and identity theft.
Data leaks have risen to an all- time high, where the average global cost of data breaches is $4.45 million in 2023. The best way to cut down breaches is to have an integrated platform for continuous sensitive data protection.
This blog talks about what sensitive data is, what is the need for sensitive data protection and we also present 5 ways to protect sensitive data. Dive in to know more.
Sensitive personal data is a cluster of information that requires protection from phishing attacks, cyber frauds, and unauthorized access to outsiders, due to its sheer importance and link to an individual’s privacy.
There is a slight difference between sensitive data and sensitive personal data. Sensitive data can be any data that are sensitive to an organization or business and it may risk the privacy of individuals, if at all breach happens.
On the other hand, sensitive personal data are those that can be used to identfy an individual using few characteristics ( or data).
GDPR encourages data security solution such as pseudonymous data or non-directly identifying information that does not allow direct identification but allows singling out of individual behaviour. GDPR encourages the use of pseudonymous information over directly identifying information as it reduces the risk of data breaches having adverse effects on individuals.
Sensituve data are sensitive in nature as, if disclosed, could result in a significant risk in terms of financial and legal for individuals, organizations, or enterprises.
Examples of sensitive personal data include personal information such as name, contact number, address (collectively PII), education records, health records (PHI), credit card information (PCI) or geolocation data(GPS, Wi-Fi, or mobile networks), among others.
Sensitive data according to EU’s GDPR or General Data Protection Regulations involves:
The data sensitivity of information depends on its weight of low to high risks if disclosed through an unauthorized source.
Such sensitive data exposure can be through 3 ways:
Availability breach is a major loss as it can lead to permanent loss of data. The three types of sensitive source breach also sum up the various ways in which sensitive data are measured.
The four types of sensitive data includes:
Further, the personal and financial information can be broadly classified into two categories:
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Read more about the 12 requirements for PCI-DSS Compliance.
Sensitive data protection is a must. Here's why:
We started by stating that ‘time’ & ‘speed’ are the ultimate factors that help in data masking techniques.
Below are a few of the steps that help in time efficiency and speed of data security regulation by protecting sensitive data from data theft.
Sensitive data discovery is the first step towards securing data at its basic premise. Data is stored across clouds and discovering its location sets the right foot towards protecting it.
Databases across the cloud estate of an organization are scourged through data discovery tools.
The cloud platforms where data are discovered from, includes Snowflake, Azure Synapse, MySQL, SQL Server, Amazon Redshift, Google BigQuery, MongoDB, Elasticsearch, Google Artifact, PostgreSQL, and Amazon S3.
Read more about Data Discovery Best Practices.
To protect data, its basic identity needs to be classified and stored.
Sensitive data classification is the process of categorizing data based on its sensitivity, importance, and the level of protection required.
Our process of data classification within an organization involves:
According to International Data Corporation, only 20% of security personnel use automated data discovery & classification tools. It has become more important than ever to automate data classification for future-proofing your data management.
Data governance is a comprehensive framework for your data mesh architecture that ensures high data quality, effective data management, data security, and regulatory compliance within an organization.
It involves defining user policies, procedures, and guidelines to manage data assets across the entire data lifecycle.
Read more about the differences between Data Governance and Data Security
A key feature of governance policy is Data Masking
To explain dynamic data masking, let’s take an example.
When you log into any website that stores your credit card information, it never reveals all the digits, but only the last four digits.
If you wish to, as the owner of the information, you can reveal the digits by pressing ‘show all’. This is possible via dynamic data masking.
Dynamic data masking is a technique of access control that protects sensitive information in a database by limiting the exposure of the data to users.
It works by dynamically masking the sensitive data in query results, making it possible to reveal only a subset of the data for certain users while keeping the sensitive part hidden.
A Data Protection Impact Assessment(DPIA) is a systematic process that helps organizations identify, assess, and mitigate the risks associated with processing personal data.
Since 2018, this is a mandatory requirement under the General Data Protection Regulation(GDPR) and similar other laws. DPIA is especially important when introducing new data processing activities, technologies, or systems that could result in high risks to individuals' privacy and data security.
The steps for conducting DPIA includes:
Attribute-Based Access Control (ABAC) enhances sensitive data protection by:
Sensitive data is growing at a rapid pace, and data breaches are everywhere from small to large scale industries. Cloud environments are sprawling with sensitive data, with easy access permissions. It is high time for businesses to consider sensitive data protection techniques.
Breaches can be prevented by keeping a check on your data inventory, classify them according to sensitive data categories or custom define them.
Further, organizations need the flexibility to govern their data with data policies and mechanisms such as ABAC.
Get all your data protection needs in a bundle with our data security platform. Let's collaborate for secure data protection framework.
Sensitive data or proprietary critical data can be primarily protected by first detecting them from all the databases and then classifying them into various categories such as PII, PCI, and PHI. Next, apply governance rules over the users to monitor the usage, while you maintain your security posture and remain ccontinuously data compliant.
Traditionally, the CISOs are held responsible for mishandling or any form of data breach. But, with OptIQ, organizations can train their employees to be individually accountable. Teams can get access when required and download sensitive information with prior authorization – all with a simple attribute based access model.
All kinds of data are important, but few are essential and core to an organization’s reputation. These are sensitive personal data of customers and employees. Labeling them based on sensitivity and placing a threat level on it as a designation helps to secure the data in a better space. This is called data sensitivity labeling.
Users of data can always see the sensitive data of customers and employees. This can be a customer manager accessing the critical data of a customer while solving his/her problem. Such scenarios can be prevented using OptIQ. Users can use the information based on their authorization levels and never access full information, as it would be redacted, shuffled or masked based on requirements.
Sensitive information can be protected in an organization by providing access to users only when required and placing a limitation on the availability of data. Data access governance and attribute based access control helps in securing sensitive data in real-time.
Sensitive data can be protected using OptIQ's data protection module where any data misuse, data exfiltration or data threats are identified in real-time and protected by swift mitigation and response.
