What is Data Access Control?

Data access controls are foundational security measures that determine who is authorized (and under which conditions) to view, use, or modify specific data resources within an organization. 

Although critical for safeguarding sensitive assets and ensuring proper data handling, these controls alone do not completely secure the data.

True data security measures require combining data access controls with other solutions, such as data tokenization, to provide comprehensive protection against ever-evolving threats.

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What are Data Access Controls?

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Data Access Controls are the security measures – i.e., processes, policies, and technologies – that regulate and restrict access to an organization's data resources. 

These controls function by defining permissions and restrictions based on user identities, roles, or other attributes, effectively managing the flow of information and limiting exposure.

This involves authenticating users to confirm their identity and then authorizing their permitted interactions with data according to set rules. 

While fundamental to data security, like building protective walls, these controls are necessary but not sufficient on their own; they should be complemented by other security strategies that anticipate potential breaches.

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How Does Data Access Control Work?

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Data access control functions via a two-step process of authentication and authorization, applied consistently across an organization's environment, including on-premises to cloud systems. 

It is both a technical procedure (requiring software configuration to implement rules) and an organizational one (needing documented policies that define who accesses what data).

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Authentication

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Authentication is the initial phase in the data access control process. Its purpose is to verify the identity of the user or entity attempting to gain access.

This verification can be achieved through various methods, such as passwords, login credentials, security tokens, biometric scans, or API keys.

To enhance security, many systems utilize multi-factor authentication (MFA), which requires users to provide more than one form of verification to confirm their identity.

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Authorization

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Following successful authentication, authorization determines the extent of access granted to the verified user. 

This step involves checking the authenticated user's credentials/attributes against predefined access policies to determine what data or resources they are allowed to access or modify, and what specific actions (like viewing, editing, or deleting) they are allowed to perform.

Authorization effectively establishes the level of access and the scope of permitted actions for each user based on these established rules and policies.

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Data Access Control Models

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Various models exist to implement data access control, each offering different methods and levels of restrictiveness to manage permissions within an organization.

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Discretionary Access Control (DAC)

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Under DAC, access control decisions are left to the discretion of the resource owner or creator. 

This person has the authority to determine who can access the resource and what permissions they have, often managed through mechanisms like Access Control Lists (ACLs).

A key feature is that users with certain access permissions can typically pass those permissions on to other users.

While this offers flexibility and empowers users to manage their data, its decentralized nature can make oversight difficult and potentially lead to confusion or security vulnerabilities if not managed carefully.

It is commonly implemented using an owner concept, as seen in Unix file modes, and is often suitable for smaller organizations or managing personal data.

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Mandatory Access Control (MAC)

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MAC is a non-discretionary approach where access decisions are centrally controlled and enforced by the system, not individual resource owners.

Access is granted based on comparing security labels assigned to resources (indicating sensitivity, e.g., "Confidential," "Secret") with the formal clearance levels granted to users.

A user must have a clearance level equal to or greater than the resource's classification to gain access. End users cannot alter these classifications or clearances, nor can they grant access to others.

This model is often employed in environments requiring high security, such as government and military organizations, due to its rigid structure, though it can be more complex to manage.

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Role-Based Access Control (RBAC)

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RBAC restricts access based on a person's role within an organization, aligning permissions with job functions and responsibilities.

Instead of assigning permissions directly to individuals, permissions are grouped into roles, and users are assigned to one or more roles.

This simplifies administration, enhances scalability, and helps enforce the principle of least privilege by ensuring users only have the access necessary for their duties. RBAC also ensures consistency by enforcing that multiple people with the same role receive identical permissions.

Roles are typically predefined based on criteria like department, job title, or authority level. When a user's job changes, an administrator assigns them a new role with the appropriate permissions.

RBAC is widely used, particularly in large enterprises and regulated industries, due to its manageability and support for audit and compliance.

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Attribute-Based Access Control (ABAC)

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ABAC – sometimes called policy-based (PBAC) or claims-based (CBAC) access control – offers a dynamic and highly flexible approach. 

Under ABAC, access decisions are made in real-time by evaluating rules or policies based on attributes associated with the user (subject), the resource (object), the requested action, and environmental conditions (like time or location).

ABAC allows for fine-grained, context-aware control by considering multiple characteristics simultaneously, enabling complex policies that adapt to changing conditions.

For example, a policy might grant access only if a user is in a specific department, accessing a certain type of document while in a designated office during business hours. 

ABAC provides significant flexibility, granularity, and scalability, making it suitable for dynamic environments, securing sensitive data, and meeting regulatory compliance requirements.

Its architecture often involves Policy Enforcement Points (PEPs), Policy Decision Points (PDPs), and Policy Information Points (PIPs) to manage and evaluate attributes and policies.

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Access Controls Offer a Start, but Zero Trust is a Must

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Access controls play a role in data security because they determine who can view or modify critical information, but today, organizations must pivot to a Zero Trust security model. 

Traditional perimeter-based security measures simply can’t keep up with today’s threats and business or operational realities. 

For example, bad actors now leverage AI to target and attack around the clock. They easily adapt to defenses while exploiting multiple vectors – from social engineering to automating vulnerability scanning – at scale.

Today’s threats don’t just breach perimeters; once inside, they rapidly move laterally and zero in on overlooked system gaps, forcing organizations into a cat-and-mouse game they can’t win.

Internally, businesses want to leverage data more than ever. They’re collecting large volumes of data and are using it to make decisions across marketing, sales, e-commerce, and many others. However, this also leads to more individuals needing access to that data, thus elevating the risk of both sensitive data exposure and compliance problems.

Overall, a more sustainable approach accepts that breaches will happen, so instead of fixating on breach prevention, leaders focus on making lost/stolen data useless to intruders. Likewise, using data for business needs shouldn’t mean exposing sensitive values; organizations can use data tokenization to deliver useful insights without exposing the real values. 

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Data Tokenization

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Data tokenization replaces sensitive data elements within structured, semi-structured and unstructured data repositories with non-sensitive placeholder values called tokens. 

The original sensitive data and its associated tokens are stored securely in a separate vault, and only the tokens are used in systems and applications.

Even if attackers gain access to the tokens, they are meaningless without access to the secure vault, significantly reducing the risk of data exposure.

Tokenization can preserve data format and type, ensuring compatibility with existing systems, and protects against both external breaches and insider threats.

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Dynamic Data Masking

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Dynamic Data Masking (DDM) works by concealing sensitive data in real-time based on user privileges or context, without altering the original data in the database. 

When an unauthorized user queries data, the sensitive portions are automatically obscured or replaced with masked values (e.g., showing only the last four digits of a credit card number preceded by **** **** ****).

This allows organizations to control data exposure granularly, enabling legitimate users to access information while protecting sensitive details from those who shouldn't see them.

Crucially, both tokenization and masking target the data itself rather than its perimeter, reducing the impact of from the successful infiltration by a cyber criminal using increasingly sophisticated methods.

Crucially, both tokenization and masking target the data itself, rather than the perimeter around it. Hence, any data breach resulting from a successful infiltration by a cyber criminal, even with the most sophisticated means available, won’t be as impactful as they wouldn’t have the actual, sensitive data, just tokens or masked values. 

They also mitigate the risks posed by insider threats and inadvertent employee errors, ensuring that even those with legitimate access can only see what they need.

When combined with robust access controls, these solutions transform data from a liability into an asset that remains out of reach even if attackers slip through the cracks.

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Start Today

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With the wider shift to Zero Trust, data-centric security is both the new and current standard for protecting data. 

Organizations that aren’t taking this approach are not only behind, but they are also targets for cybercriminals. Not only are bad actors using more capable tools to both expand and intensify their attacks, but as other organizations adapt with data-centric security and frustrate criminals, those same attackers will actively target organizations that don’t secure their data directly.

This approach isn’t about abandoning firewalls or identity management; rather, it’s an evolution that accepts the reality of modern threats and prepares for them more effectively. Moreover, it’s about moving beyond endless anxiety over the next phishing threat and/or zero-day exploit and, instead, ensuring your sensitive data remains safe, even in the event of a breach.

Whether you’ve begun planning for data-centric security or are looking to augment current Zero Trust implementations, schedule a demo with DataStealth today. 

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For More Information on Data Security, See:

• What is Data Encryption?
• What is Data Discovery?
• What is Data Classification?

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