Fundamentals of Data Access Management

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data access management

Across all industries, and in businesses of all sizes, one thing holds true: The more you know about your customers, the greater your organization’s chances for success. This makes data your company’s most valuable non-human asset. As the revenue potential of data increases, the job of protecting that data becomes more important and more challenging. To safeguard their data assets, companies refocus on data access management as the cornerstone of their overall data strategy.

Data access management is also referred to as data access governance or identity and access management. All three concepts share the goal of restricting access to the organization’s sensitive data. This includes personally identifiable information (PII) and other customer information, as well as protected health information (PHI), confidential personnel information, financial information, and trade and proprietary information. 

Another vital component of data access management is protecting the credentials – primarily user IDs and passwords – that authorized users rely on to access the data their work depends on.

What Is Data Access Management?

Data access management is a key aspect of the organization’s overall Data Management practices, which encompass all aspects of acquiring, storing, using, protecting, and deleting information that comes into the company’s possession, regardless of the source of that information. The overarching goal of Data Management is preserving the integrity of the data by maintaining its accuracy, consistency, and reliability throughout the data life cycle. Aspects of Data Management include validation, error detection and correction, backup, and restoration. 

An important component of a firm’s Data Management strategy is controlling access to the data to prevent data corruption, data loss, or unauthorized modification of the information. The fundamentals of data access management are especially important as the first line of defense for a company’s sensitive and proprietary data. 

Data access management protects the privacy of the individuals to which the data pertains, while also ensuring the organization complies with data protection laws. It does so by preventing unauthorized people from accessing the data, and by ensuring those who need access can reach it securely and in a timely manner. 

Achieving these goals combines five Data Management components:

  • Access control measures include managing user IDs and passwords, applying biometric authentication systems, using multifactor authentication (MFA), and switching to subscription-based access control via the cloud.
  • Data classification and handling identifies three or four levels of protected data, such as public, proprietary (private but low sensitivity), confidential (such as trade secrets, contracts, and employee reviews), and sensitive (legal, financial, or other areas that would be jeopardized by a data breach).
  • Secure data at rest and in transit by locating, classifying, and encrypting the data wherever it’s stored and whenever it’s transmitted over the network. Data access control depends in large part on user training and education related to data collection practices and acceptable data use.
  • Select access control tools and procedures as outlined in the U.S. National Institute of Standards and Technology (NIST) Privacy Framework, which covers inventory and mapping, risk assessment, business environment, governance, risk management, awareness and training, and monitoring and review.
  • Employee training should cover use of tech products and services, managing passwords, identification and proper handling of sensitive data, incident response, social engineering methods, and best practices for data security. 

Why Is Data Access Management Important to Organizations, Employees, and Customers?

The “democratization” of data analytics tools presents companies with an opportunity to put powerful analysis capabilities in the hands of business managers and other employees. This has the potential to improve decision-making while minimizing reliance on high-salaried data scientists. 

However, self-service data analysis creates risks for organizations because untrained staff may not properly validate the data and the processes used to analyze it. As a result, the analysis can lead to incorrect assumptions and misinterpretation of findings.

Implementing strong data security and access controls protects all elements of a firm’s IT infrastructure. The benefits of a solid data access management strategy impact all areas of a business’s operations:

  • Access to sensitive data can be limited by role or permissions, and tracked for auditing purposes.
  • At any moment, you have visibility into who is accessing which data.
  • The data is protected against accidental or intentional deletion or overwriting.
  • You can confirm compliance with applicable privacy and data security regulations.
  • Strong access controls deter unauthorized disclosure of sensitive and proprietary data.
  • Controls can be applied based on physical or virtual zones, time, role, application level, count, or other factors.

Appropriate data access controls improve the efficiency of business processes by limiting the number of actions an employee can take. This helps simplify user interfaces, reduce database errors, and automate validation, accuracy, and integrity checks. 

By restricting the number of entities that have access to sensitive data, or permission to alter or delete the data, organizations reduce the likelihood of errors being introduced while enhancing the effectiveness of their real-time data processing activities.

Data Access Management Best Practices

An organization’s access control policy defines how and when specific data will be available to view, change, or delete. The policy covers five key areas:

  • User identification may be unique usernames, employee IDs, or biometric identifiers.
  • Authentication validates the user’s identity, whether using passwords or passphrases, security tokens, or biometric verification.
  • Authorization sets the level of access the user is granted based on the principle of least privilege, which allows access to only the operations that the person’s role or task requires.
  • Access control methods include discretionary access control (DAC), mandatory access control (MAC), and role-based access control (RBAC).
  • Auditing and monitoring confirm that your access controls are functioning as designed and are updated regularly to address new needs and threats.

Becoming a data-driven organization requires overcoming several obstacles, such as data silos, fragmented and decentralized data, lack of visibility into security and access-control measures currently in place, and a lack of organizational memory about how existing data systems were designed and implemented. 

Devising an effective and comprehensive data access control policy encompasses mobile and biometric authentication, cloud-based management, integrations with other business systems, and emergency preparedness.

These are among the components of a data access management strategy:

  • Establish limits on the internal use of data based on employee roles (RBAC).
  • Log and track data when stored and in use to support auditing and security monitoring.
  • Adopt least-privileged access controls that provide users with only the data access they require.
  • Categorize, inventory, and map data continuously to confirm it receives the appropriate level of protection.
  • Enter into data processing agreements (DPA) that stipulate the rights and obligations of internal and external parties engaged in any data processing activities.

Overcoming the Challenges of Managing Data Access

The value of data to businesses increases as the amount of data explodes and the capabilities of data analysis tools and techniques are enhanced. Skyrocketing data levels and more sophisticated analytics present formidable challenges to the people charged with protecting and maintaining this valuable resource. 

However, for every challenge posed by data access management, a range of solutions are available.

  • Growing data amounts are addressed via raw data processing, automated data categorization, and data accuracy checks that eliminate redundant or superfluous data to keep total levels in check.
  • Connecting data related to customers, products, suppliers, and markets helps remove data silos that restrict access and transparency, and that cause data redundancy and update errors.
  • Implementing Data Quality monitoring standards lets you identify and update data that is out-of-date, inaccurate, or no longer fulfilling its purpose, which improves decision-making and enhances efficiency.
  • Ensuring that data is easy to integrate with other business systems maximizes the data’s value to the organization and makes data analytics tools more accurate and effective.
  • Adopting machine learning and other AI techniques automates some analytics processes and addresses the shortage of data scientists.
  • Integrating Data Governance with access management creates a single set of policies and procedures for the correct application of data-related activities.
  • Similarly, by combining encryption and other security measures with data access controls, organizations are able to monitor the actions taken on the data as well as the parties taking, or attempting to take, those actions.

The Role of New Technologies

Keeping pace with technological developments is a never-ending challenge for organizations of all types. Data access management will be impacted by five recent developments:

  • Zero-trust security frameworks build on least-privileged access approaches by requiring user validation after the initial login when accessing sensitive data resources.
  • Enhanced multifactor authentication adds automatic checks of usage patterns to the standard use of two or more systems or devices to verify a user’s identity.
  • Decentralized identity ecosystems reduce the threat of damage due to identity theft and other privacy leaks by relying on blockchain technology to preserve a user’s identity.
  • Machine learning and AI-based access controls promise to improve the accuracy of user identification by monitoring user actions, behaviors, and authentication transactions to anticipate and respond to anomalies or potential data breaches.
  • User consent and compliance are being integrated with data access management to streamline processes subject to privacy regulations such as Europe’s GDPR, HIPAA, and various state privacy statutes.

The fundamentals of data access management continue to evolve as the needs of users change and new threats to data’s safety and validity arise. Companies that embrace the challenges of managing access to their valuable data resources have the best chance of gaining a competitive advantage by realizing the benefits and potential of integrated and automated Data Management frameworks.

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