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Exploring the Intersection of Data Privacy and Data Security

Published September 20, 2021 / Updated January 8, 2024
Author

Omer Imran Malik

Data Privacy Legal Manager, Securiti

FIP, CIPT, CIPM, CIPP/US

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This post is also available in: Brazilian Portuguese

The transformation of the digital landscape has helped to clear the confusion between data being an asset rather than a commodity. Data is a “non-fungible” asset, as Jacky Wright, the Chief Digital Officer at Microsoft, puts it. It cannot be replaced as it is unique to every individual, entity, or object. More importantly, data is the driving force behind innovations and technological advancements.

As much as it is true that data brings a multitude of opportunities to organizations globally, the fact also remains that it equally brings some serious security and privacy threats. The 2017 Equifax data breach can be a great point of reference as the breach affected 147.9 million consumers, costing the company over $4 billion dollars.

Organizations globally are collecting and producing a greater volume of data than they can handle. The high volume of data exists across multi-cloud platforms, SaaS, and on-premises systems. This unstructured, untracked, unmapped, and seemingly, unprotected data creates opportunities for threat actors to siphon and exploit it.

Therefore, it is vital for organizations to protect all the data they own, monitor, or store - be it personal data or sensitive data. Organizations also have the responsibility to collect, process, or use the data in compliance with global regulations, such as GDPR, CCPA, PIPL, etc. Failure to do so could result in security breaches, hefty fines, and more importantly, loss of consumer trust.

But how should organizations tackle the data security and privacy threats and challenges?

Tackling Data Security and Privacy Risks

Organizations globally are working with a huge volume of data, and IDC predicts that the volume will grow ten times by 2025 and due to the absence of an effective data intelligence and privacy mechanism in place organizations find it difficult to track, monitor, and protect data privacy effectively and minimize the risk.

With privacy regulations like GDPR requiring companies to record how they collect information, store it, use it, monitor it, or archive, it is vital to break data silos and ensure optimal data classification, cataloging, risk management, data mapping, and consent management.

To ensure that the security and privacy risks are kept in check, organizations should first start with:

Determining Data Locations

Organizations should start the data discovery and assessment process by finding and determining where the data resides. It could be in a number of data assets, such as cloud IaaS/PaaS servers, SaaS applications, and on-premise systems.

Discovering Existing Data

The second most important step an organization should take is to identify and track the personal or sensitive data that they own. It could be in structured or unstructured form scattered across multiple devices, platforms, and applications. Once the data is discovered, it should be classified and labeled for different use cases.

Assessing Data Security and Privacy Postures

Once the data assets are located and the existing data is discovered and labeled, the next step should be to evaluate the security and privacy posture of data. The data needs to be scanned for any security misconfiguration, such as the absence of encryption or access control policies. The process allows organizations to gain better visibility on data security, find any gaps, and adapt security controls for security assurance and privacy compliance.

Identifying Data Controllers and Processors

The next step is to determine who currently has access to data and if the person needs access. As discussed above, access abuse, data leaks, and accidental data exposure are often associated with poor control access policies. Therefore, it is important to identify people with access to certain data so that the organization can track, monitor, or strengthen access control on confidential data.

Once the data is classified, labeled, and cataloged, organizations can proceed to address security, privacy, and compliance issues. Starting with:

Control Access

Control access is an integral part of a company’s IT security environment. There should be pre-defined principles involving user identity management and access authorization management. Strict policies should be defined for adding users to the system and authorizing least privileged access to resources.

Automate Controlled Access

Job roles of some employees often change, prompting the organization to modify or reconfigure access control. Sometimes, security loopholes arise when some employees leave the company while their access control remains unchecked. This creates a security gap, giving threat actors an opportunity to exploit.

Through effective automation, organizations can automatically update, revise, or revoke access permission of employees whose job roles have changed or who have left the organization.

Privacy regulations like GDPR require companies to collect and record consents from users. Companies leverage consent as one of the legal bases for collecting users’ data, processing it, and using it.

After mapping the data and discovering all the end points, companies should devise effective methods to collect users’ consent. The consent record should contain a timestamp when the consent is collected, along with the source details and the policies that define consent processing for specific purposes. Companies should also have mechanisms in place to honor the consent withdrawal requests from users.

Privacy Policy and Notice Management

Global privacy regulations mandate organizations to maintain a privacy policy and privacy notices, containing details regarding data collection, processing, retention, and other essential metrics.

Data Subject Requests Mechanism

As per privacy regulations, data subjects have the right to request a copy of their data, data processing reasons, data rectification, and data deletion. To honor data subjects’ requests, companies need to have an easy-to-use system that can collect, process, and fulfill data subjects’ requests in a timely manner.

Data Breach Management

In the event of any security or privacy breach, organizations must notify the regulatory authorities about the breach and the affected data subjects. Having a comprehensive data breach response plan would help in avoiding and mitigating risks.

Assessing Processing and Vendors’ Privacy posture

Organizations should also maintain an evaluation of a vendor’s compliance readiness with global regulations to avoid privacy-related risks arising from their vendors

Automation is the Way Forward for Data Privacy, Security, and Compliance

Organizations are handling and processing a staggering amount of data. This data is distributed across a wide variety of systems, applications, SaaS platforms, and multi-cloud networks. This enormous amount of data ultimately causes data sprawl.

Data sprawl not only makes it difficult for organizations to keep track of data, retrieve it for specific purposes. It also leads to further organization-wide chaos, such as mishandling of information, access abuse, excessive and unmonitored privilege access.

The same data is then stored in different locations, either in structured or unstructured form, adding more confusion and security risks. Identifying such data, classifying it, and deriving meaningful insights out of it isn’t possible through manual processes. In fact, a manual process could lead to wasted time, human errors, and, thus, cybersecurity and compliance risks.

A viable solution to this problem is automation. Through machine learning and Artificial Intelligent-powered automation, companies can reduce human errors to a minimum, enable effective data intelligence and consent management, and ensure complete compliance with security and privacy regulations globally.

Securiti is Offering AI-Driven Data Privacy, Security, and Compliance Solutions

Securiti enables organizations to identify and map structured and unstructured data, identify data risks, fulfill data subjects’ rights, fix security gaps, and ensure regulatory compliance via an AI-powered robotic PrivacyOps platform.

Data Security

Securiti helps organizations identify data risks, automate access control, and secure data.

Discover Data Risks: Scan systems for data risks that could lead to security breaches.

Protect Data: Set rules for classifying and labeling data for encryption and protection.

Control Access: Set policies to automatically add, update or revoke access permissions.

Data Privacy

Securiti empowers organizations to effectively manage the following privacy obligations:

Data Mapping: Keep track of your data and catalog all assets and properties under one roof.

Consent Management: Use mapped data to honor users’ consent requests.

DSR Fulfillment: Automate people’s data requests and keep a record of compliance.

Privacy Policy and Notice Management: Automate publishing of privacy notices and enable centralized management by tracking and monitoring privacy notices in order to maintain compliance.

Data Breach Management: Automate data breach incidents diagnose breach notifications to concerned stakeholders.

Vendors Assessment: Keep track of privacy and security readiness for all your service providers and processors for compliance purposes. Global Regulations Compliance

Securiti helps organizations harness the power of AI for automating data security, privacy, and consent management to ensure regulatory compliance globally.

Frequently Asked Questions

Privacy and data security are closely related. Data security measures are put in place to protect data from breaches and unauthorized access, which, in turn, ensures the privacy of individuals' personal information. Security safeguards help maintain the confidentiality and integrity of personal data, supporting privacy rights.

The intersection of privacy and cybersecurity focuses on protecting sensitive information from unauthorized access or breaches. It involves implementing security practices, policies, and technologies to safeguard personal data, thus preserving individual privacy and data protection.

Yes, data privacy is a component of data security. Data security encompasses various measures to protect data, including encryption, access controls, and threat detection, which all contribute to preserving the privacy of personal information.

Technical components of data privacy and security include encryption techniques, access control mechanisms, data classification and tagging, security audits and monitoring, intrusion detection systems, and incident response procedures. These components work together to ensure data protection and privacy compliance.

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