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Data privacy is a growing concern for consumers. In a recent survey by KPMG, consumers reported feeling increasingly uneasy about the data collection practices of corporations. They understandably want to safeguard their personal information and ensure that organizations do not share it or sell it without their permission.

SEE: GDPR security pack: Policies to protect data and achieve compliance (TechRepublic Premium)

As a result of the increasing concern over consumer data privacy and protection, many government regulations and compliance mandates now focus solely on consumer data protection. Businesses in countries around the world must comply with these regulations or risk heavy fines to the tune of tens of millions of dollars in collective enforcement. In order to maintain both customer trust and regulatory compliance, learn how to enforce privacy and compliance mandates here.

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The high price of non-compliance

Under the European Union’s General Data Privacy Regulation Act, data protection authorities are empowered to impose fines of up to €20 million (roughly $20,372,000) or 4 percent of worldwide turnover for the preceding financial year — whichever is higher.

GDPR mandates help to protect consumers’ personal, financial and behavioral information, giving consumers the right to demand access, privacy, non-disclosure, non-sale or non-use of their data. They also have the “Right to be Forgotten,” which gives individuals the right to ask entities that hold their data to delete it. Similar regulations, including the California Consumer Privacy Act have been enforced in the United States, with continued regulations rolling out across other regions.

Since the inception of these various consumer data privacy compliance acts, global organizations across diverse industries have faced a common challenge in protecting consumer data to remain compliant. The enforcement of these mandates at scale requires automated solutions powered by artificial intelligence. Yet, it’s important to understand an added challenge: Not just one but several of the tasks involved in the compliance process require intelligent automation.

A full-stack answer to data compliance problems

Organizations can implement a comprehensive set of data compliance mandates cost-effectively when they use artificial intelligence to automate key processes. While traditional solutions exist to potentially address data privacy issues, they are seriously lacking in their ability to enable intelligent automation of these compliance-focused tasks.

Traditional solutions may be able to cope with the current volume of consumer privacy requests; however, as consumers become increasingly aware of their rights, the number of such requests will increase dramatically, thus necessitating an automated approach in order to scale cost-effectively.

To address this complex issue, it is essential to turn to new technologies like deep AI-based innovations to automate compliance and privacy enforcement across the enterprise, regardless of where the data resides.  This method delivers a comprehensive way to eliminate the limitations of traditional data discovery, all while perfecting accuracy, performance and maintainability. Deep AI technologies help companies meet this challenge while minimizing risk and large financial penalties.

SEE: How does data governance affect data security and privacy? (TechRepublic)

As you might guess, this is a daunting task that involves several complex processes. It requires the ability to sift through a large volume of data corpuses, both on-premises and in the cloud, at a very high rate. In order to do this, you need a high level of intelligent automation.

The following sections describe three foundational technologies and features — automated discovery, automated data mapping and automated data service request handling — that need to be integrated to effectively automate compliance efforts while keeping costs down.

Automated discovery

An AI-powered data discovery engine can address the deficiencies and constraints of legacy data discovery solutions. Previously, the tools and methods that were developed and deployed were meant to enforce specific compliance measures only, such as SOX for corporate data compliance, PCI/PII for payment card industry verticals, HIPAA for the healthcare industry, and several other mandates against theft and/or unauthorized disclosure of confidential enterprise and individual data.

With the emergence of new consumer data privacy compliance mandates such as GDPR and CCPA, tools and processes are now required to enforce appropriate security and privacy measures against not only theft but also unauthorized disclosure or usage of confidential consumer information. Automated discovery enables data discovery in real time across all regulatory compliance mandates, including GDPR, CCPA, HIPAA, PCI, PDPB, PDPL and other data privacy laws across the globe.

The emergence of newer data security and compliance regulations requires users to properly handle increasingly varied and complex data types and structures. These may involve simple keywords (tags or labels) for complex regular expressions, as well as complex composite data objects, composed of more than one type of primitive data object.

SEE: Data governance checklist for your organization (TechRepublic Premium)

Consumer data compliance and privacy enforcement require the ability to accurately discover a complex set of relevant information in a large corpus. To this end, companies need eDiscovery technology that automates the ability to define newer types of complex data objects to support a wide variety of current and future data objects discovery.

While some systems rely on simple keyword, lexical matches or regular expression-based pattern-matching techniques, these are insufficient and far too error-prone for the automated identification of more complex data objects. Instead, the system needs sophisticated data identification techniques that can perform automatic data identification for virtually any type of complex data object.

Traditional data classification systems that require manual processing are ineffective, error-prone and unscalable. Therefore, the eDiscovery system must also be able to recognize and auto-classify confidential and/or compliance-mandated data in any format.

Automated data mapping

In the context of data privacy, data mapping relates to the tricky task of creating an inventory of all relevant information that exists in an enterprise’s corpus, then mapping it out over the enterprise’s data infrastructure. The best way to do this is via an automated data mapping system that creates a persistent map of the data/information objects that exist in enterprise data sets.

This is a crucial capability that facilitates efficient navigation through large storage systems and corpuses, following the lineage of any data of interest. A data map greatly facilitates the compliance enforcement process, serving as a crucial input to the compliance enforcement workflow generation process.

Automated data service request handling

Another foundational component of a modern-day consumer data compliance and privacy enforcement system is the ability to automatically handle data service requests in a timely and scalable fashion. A data service request handler should be able to incorporate the automatic generation of data subject request workflows. DSR workflow creation is a critical and complex process that requires knowledge of the:

  • Data map: The distribution of data objects over the entire data corpus structure of the enterprise.
  • Accessibility map: A jurisdiction layout for IT staff over the various data corpus and repositories.
  • Task breakdown structure: A deep knowledge of how a specific type of DSR can be broken down into a set of primitive tasks required to complete the enforcement of a DSR.

Traditional DSR systems are typically limited to manual intervention, which is not only tedious but also prone to inaccuracies. Instead, a data service request handler should be capable of incorporating the automatic enforcement of DSR task primitives. For the timely execution of a DSR, the system must automatically implement all of the constituent DSR tasks within the prescribed time frame, which requires intelligent automation of the DSR task execution process.

Next-gen comprehensive compliance and privacy enforcement solution

The result of automating discovery, data mapping and data service request handling is a unified next-generation compliance and data-privacy enforcement solution. This solution has the power needed to automatically identify content, classify it and generate privacy enforcement policies in real time. It eliminates the need for constant tedious manual intervention.

When compliance officers and other trained data compliance professionals put AI automation to work, companies can remain compliant with consumer data privacy mandates in a way that does away with manual pre-processing costs and enables protection against human error and malicious acts. There’s no better way to provide real-time enforcement of data privacy mandates in an ever-changing regulatory landscape.

Tarique Mustafa
Tarique Mustafa

Tarique Mustafa is the founder, CEO and the “Brain” behind GhangorCloud’s game-changing technology and product. He is recognized in the industry as a leading visionary and expert in information security, advanced persistent threats and data leak prevention. Tarique’s groundbreaking innovation in advanced persistent threat and “Malicious Data Leak Prevention” has won international recognition.

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