7 Ways Good Data Security Practices Drive Data Governance

Organizations generate more data logs daily owing to their digital business operations. As more and more businesses undergo the digital shift, maintaining the safety of this massive volume of sensitive information is bound to take time and effort. 

One of the reasons confidential data may go unprotected and unaccounted for is due to poor internal data management procedures. While DevOps teams and DBAs try to stave off the risks by testing applications in cloud-hosted architectures using “shadow IT” and “shadow data” outside of their IT framework, these actions may still cause significant problems in data governance. This is why it is important to be aware of the industry-best data security practices and implement them in time to secure your data infrastructure from breaches and other malicious cyberattacks.

The management of your organization’s data availability, usefulness, integrity, and security through privileged access control are all made possible by good data governance. This blog will outline seven ways good data security practices can help an organization drive data governance.

What is Data Governance?

Data governance refers to a collection of policies, rules, and practices that aid organizations in better understanding and utilizing data. Organizations can define data ownership, implement security measures, uphold data quality, consistency, and accuracy, and enhance interoperability with data governance. All of these are covered by a solid data governance approach that enables firms to grow while maintaining compliance with laws like the GDPR, CCPA, and HIPAA, to mention a few.

Now let’s check out what best practices your company should follow to ensure good data governance.

7 Ways Good Data Security Practices Drive Data Governance 

  1. Outline Data Ownership and Stakeholders

You need a data governance team with clearly defined ownership and duties to build a strong governance program. Organizations must divide ownership among different people and divisions. Organizations can see their data workflow, security posture, and lifecycle by defining and comprehending the accountability and authority against various data domains. It also streamlines the governance structure more effectively, promoting seamless interaction across teams and departments so they can face difficulties together.

Depending on its size and corporate goals, an organization’s governance team may have many leaders. However, the following individuals are the main participants in any governance program —

  • Head of Data Operations (CDO):

The Chief Data Officer (CDO) heads any organization’s governance program hierarchy. The creation, execution, and performance of a governance strategy fall under the CDO’s higher degree of authority and accountability. In certain organizations, CDOs also serve as data managers, leading the governance team and monitoring performance indicators.

  • Committee on Data Governance:

The governance committee oversees data champions and owners and reports to the CDO. The committee is responsible for planning the program’s policies and procedures, communicating information down the chain of command, and resolving escalated team conflicts. The technologies the data champions and owners need to conduct may frequently be decided by and implemented by the governance committee.

  • Data Owners

Data owners are in charge of how the data is used and processed and ensuring they abide by the rules and guidelines that the governance committee and the data champion have given them.

  • Data Stewards

They are primarily in charge of upholding the governance plan in the long run and making sure the data owners abide by it. Data stewards or ” champions,” as they are more commonly referred to, typically have expertise in particular data topics. To guarantee efficient governance, data stewards can manage the current team of owners and train new owners.

  1. Define domains and subdomains for accountability

Some businesses thoroughly approach data governance. As a result, a comprehensive approach slows down the execution of the governance process overall due to the massive volume of data that needs more useful categorization. Therefore, organizations must step back, define, and then prioritize the data domains essential for achieving business goals.

Data domains are essentially the higher-level classification of an organization’s most “important” data. Thanks to strategic categorization, the governance team can further designate data stewards with duty and accountability for their respective domains. Each organization has five to ten different data domains. However, it is highly recommended first to identify and implement the top 2 or 3 domains; only after these have been implemented successfully should you scale further.

The granularity level of the data domain must also be addressed when defining it. For instance, Employee Mailing Address may seem too specific a topic in any commercial context, whereas Human Resources may seem too general. It is in the organization’s best interest to connect the categorization (domains, sub-domains, or sub-sub-domains) with the business objectives to achieve effective categorization. In that regard, it should be emphasized that, due to different roles, a domain may have a single data steward or numerous stewards.

  1. Gain complete visibility of all data parts of your infrastructure

Business-critical data is often distributed amongst on-premise systems, SaaS apps, multi-cloud object storage, and even legacy and custom software. No organization can manage any data if it doesn’t know where it is located within its web of resources, systems, and applications and where it has come from.

You must be able to see everything as you are in charge of it all. Your response must take a complete data overview into account. To continually monitor your whole data repository, you must be able to view all formats, in all locations, at all times, from a single source. A thorough inventory of managed and shadow data assets provides insights into the assets’ location, security status, such as whether or not they are encrypted, and other pertinent information, such as vendor data. Organizations can find the data required to support their business objectives, such as data analytics, risk management, data protection, or compliance evaluation, by having a centralized catalog of all their data assets.

This is essential to data governance and security, in addition to the former.

  1. Control data access privileges stringently

The main part of a governance framework is setting up access governance. One of the cornerstones of your data security plan is understanding who and to what extent people have access to critical information. You must actively find and manage privileged accounts and sensitive assets to maintain comprehensive visibility and control. 

Your solution must automatically detect policy violations in real time and identify all sensitive actions to confirm their authorization. Your solution must also halt erroneous activity when violations happen or issue a warning. It should also enable you to identify malicious user activity that is the root cause of unusual behavior and analyze abnormal behavior.

Proper access to vital sensitive data or data assets can stop insider threats, unauthorized data exposure, and other online dangers. You should be able to choose the type of boundaries, in this case, access controls, that need to be put up around the business and sensitive data by analyzing the sensitivity level and security concerns. Set up least privileged excess and role-based access control as part of the access governance to lower risk.

  1. Maintain adherence to compliance standards

While compliance adherence is not all data governance, demonstrating data compliance is vital. In most high-profile data breaches, the victim organization is found to have followed data management compliance guidelines—which somewhat lessens their liabilities. Your system must make it simple to create reports that automatically track who accesses data, why they do so, and how they use it. With this degree of openness, you can act on unusual behavior above and above what compliance regulations require.

  1. Verify affiliations with third-party service providers and monitor insider threats

It is still your duty to secure sensitive consumer data even if your company distributes it with outside parties. You must ensure that your partner’s data security and privacy practices are consistent with your own to protect the integrity and security of this data.

Since insider threats are frequently hidden from perimeter security solutions like firewalls and intrusion detection systems, they can be challenging to detect or avoid. Nevertheless, effective risk management is a requirement of excellent data governance. For your incident response teams to effectively reduce insider threats, your system must offer anomalous behavior discovery, strict automatic enforcement of privileged data access, and ongoing employee education.

  1. Reduce overhead costs and resources

Many people need to work on maintaining and sustaining the comprehensive framework known as data governance. According to a survey conducted by a management consulting firm, a typical mid-sized organization might spend anywhere between $20 and $50 million on maintaining a data governance program, lowering risk, and ensuring ongoing data quality.

It is strongly advised to switch to an automated governance model to eliminate overhead costs, inconsistencies, and errors frequently seen in a traditional governance framework. Automation facilitates real-time monitoring, expedites deployment, and lowers human mistake rates.

Security and Compliance with Akitra

Establishing trust is a crucial competitive differentiator when prospecting new SaaS businesses in today’s era of data breaches and compromised privacy. Customers and partners want assurances that their organizations are doing everything possible to prevent disclosing sensitive data and putting them at risk, and compliance certification fills that need.

Akitra offers an industry-leading, AI-powered Compliance Automation platform for SaaS companies. Using automated evidence collection and continuous monitoring, together with a full suite of customizable policies and controls as a compliance foundation, our compliance automation platform and solutions help our customers become compliance-ready and certified for security and compliance frameworks like SOC 1, SOC 2, HIPAA, ISO 27701, ISO 27017, ISO 27018, PCI DSS, GDPR, NIST CSF, NIST 800-53, NIST 800-171, CMMC, FedRAMP, and more such as CIS AWS Foundations Benchmark, etc. In addition, companies can use Akitra’s Risk Management product for overall risk management for your company, Trust Center, and AI-based Automated Questionnaire Response product to streamline and expedite security questionnaire response processes delivering huge cost savings. Our compliance and security experts will provide customized guidance to navigate the end-to-end compliance process confidently. 

The benefits of our solution include enormous savings in time, human resources, and cost savings, including discounted audit fees with our audit firm partners. Customers achieve compliance certification fast and cost-effectively, stay continuously compliant as they grow, and can become certified under additional frameworks using a single compliance automation platform.

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