What are data security controls?
Data security controls are security policies, technologies, and procedures that protect data from unauthorized access, alteration, or loss. They detect and respond to potential threats, enable secure access for authorized users, and ensure compliance with any applicable regulations.
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Why do you need data security controls?
Data is at the heart of every business today—including yours. Whether it’s customer data, employee data, trade secrets, code, or other IP, you need it to keep functioning normally. And if your data falls into the wrong hands, there could be serious consequences like ransom payments, loss of control over critical infrastructure, fines, and loss of public trust.
But data security doesn’t just mean locking out intruders. The CIA triad helps us visualize and understand all of the ways that organizations need to protect their data:
Confidentiality means keeping certain information private. There are different levels of privacy for different types of data. Encryption and access controls are two ways to keep data confidential.
Integrity means that you can rely on the data to be accurate, complete, and up-to-date. Checksums and version control are two ways to ensure that data is complete and unaltered.
Availability means that your data is reachable at all times to anyone with a legitimate business need. Backups and disaster plans are two ways to keep data available even under extreme circumstances.
In this post, we’ll examine why data security controls are such an important part of your security overall strategy, how they bolster your resilience while helping you meet a variety of compliance standards, and examples of common security controls that protect data. Then, we’ll explore a tool-based approach that can vastly simplify security controls and compliance with regulations.
Data security controls and compliance
Data security is essential to meet a range of compliance regulations. These include HIPAA, GDPR, PCI-DSS, and many other security standards across a range of industries and geographic areas. Most of these standards aim to bring how you protect your data in line with best practices.
With increasingly stringent regulatory requirements around data privacy—along with a risk of hefty fines—prioritizing compliance efforts with data security controls can safeguard your operations and reputation.
One reason standards are becoming more common is that storing data in the cloud creates extra risks:
Cloud data can multiply and scale rapidly, making it difficult to control.
Cloud shared responsibility models and security defaults may leave your data at risk.
Lack of continuous monitoring could lead to unprotected shadow data.
Cloud data is often shared with third parties, including both human and non-human identities, such as machines and services like APIs, creating new areas where your data may not be secure.
Another area of risk is open-source software, which has led to an alarming rise in software supply chain attacks. Threat actors modify software components and libraries, introducing malicious code that can manipulate or corrupt your business’s crucial data.
But there’s one more huge—and growing—risk: AI. In the next section, we’ll see why AI poses such a big risk and how data security controls can help you implement AI more securely.
Data security and AI
AI has so much to offer businesses of all kinds: Many organizations are already using AI for everything from strategic analytics to customer service chatbots. On the other hand, it also creates entirely new types of risk.
AI uses data to train models, as well as for analytics purposes. And generative AI can interact with employees or users—as long as you’re sure that the data it provides is true and helpful. There are four general areas of AI-based data risk:
Data risk: Vulnerable data can be compromised, leading to leaks, manipulation, or exfiltration.
Model risk: Attackers could manipulate AI models for malicious purposes, potentially altering or extracting sensitive information.
Operational risk: Attacks like DoS or supply chain threats can disrupt AI operations.
Ethical/compliance risk: AI risks include biases, lack of explainability, and hallucinations.
Types of data security controls
The U.S. National Institute of Standards and Technology (NIST) defines four main categories of security controls:
Preventative: Identifying and remediating vulnerabilities before they can be exploited
Deterrent: Dissuading attackers by reducing the motive and profit of an attack
Detective: Identifying and warning security teams of an attack or attempted attack
Corrective: Eliminating potential harm of security incidents and establishing incident response plans
Let’s take a look at some of the data security controls available to you in each of the categories of the CIA triad (confidentiality, integrity, and availability). This list isn’t comprehensive, but it will help you start thinking about the security controls you need within your own organization.
Confidentiality controls
These controls protect sensitive information from unauthorized access. They prevent breaches that can lead to financial loss, reputational damage, and legal consequences.
Instituting encryption to control who can decode and view information. Block unauthorized users from reading data even if they can intercept it. This includes encrypting AI training data.
Maintaining access control lists (ACLs) to enforce entitlements. Cut unauthorized access to your most sensitive data.
Implementing multi-factor authentication (MFA). Ensure that compromised login credentials won’t work, keeping data more secure.
Using data masking techniques for AI. Protect privacy and avoid ethical risk by concealing any sensitive data, like personal or private information, used in AI models.
Integrity controls
These controls ensure that the data you’re using remains accurate, reliable, and unchanged by unauthorized users. With integrity controls in place, you can trust your data as the basis for decision-making and operations.
Applying digital signatures and hashing. Prevent tampering and verify the authenticity of cloud-based data.
Implementing version control. Block unauthorized changes and keep track of authorized changes to provide a check against insider threats. For AI models, this ensures consistency and blocks accidental or unauthorized modifications.
Enforcing data validation rules. Ensure that all your data is meaningful and useful (for example, that all email addresses are formatted correctly, or that inventory numbers, pricing, or salaries are within a reasonable range of values).
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Availability controls
These controls make sure you can get your hands on your data whenever you need it—whether that’s to support business operations, for decision-making, or to meet compliance requirements.
Implementing regular backups. Recover faster after data loss or corruption due to technical failures, security breaches, or natural disasters. Backup systems are essential for continuity of AI operations.
Storing data on high-availability file systems. Keep data accessible with redundant hardware and software components.
Monitoring networks and systems. Get alerted of any potential disruption with continuous monitoring and intrusion detection systems.
Physical controls
Organizations today sometimes overlook vulnerabilities in on-premises infrastructure. Controls in this area include scoping physical environments for potential risks. Are computers left on with full access while employees go for lunch? Do HR employees pay attention to essentials like locking filing cabinets? Physical controls are still essential wherever employees interact with sensitive data.
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Wiz DSPM
The best way to ensure confidentiality, integrity, and availability of data in the cloud is with data security posture management (DSPM).
DSPM tools systematically identify, analyze, and mitigate the risks associated with data breaches, unauthorized access, and non-compliance with data protection regulations. As part of your overall security posture management, DSPM capabilities include data lineage tracking, data risk prioritization, and data privacy and compliance heatmaps.
But while standalone solutions are available, DSPM is even more effective when integrated within a cloud native application protection platform (CNAPP) such as Wiz.
Your data doesn’t live in a vacuum. Unusual network traffic patterns, unexpected logins, or strange behavior patterns could indicate a security incident that puts your data at risk. Wiz is your unified risk engine, handling the widest possible range of risks, including DSPM for data risks.
From a single pane of glass, you can survey the big picture of your entire security posture. Wiz cuts the number of alerts your teams have to handle and provides graph-based context that helps them resolve issues faster. And you’ll get uniform, consistent coverage, letting you implement data security controls across diverse cloud providers.
Wiz data discovery is completely agentless, making it as simple as possible. Once it’s in place, continuous data compliance management and context-rich alerts offer you a holistic, end-to-end security approach.
And because Wiz covers you against emerging security risks associated with AI and generative AI, you can rest assured that your CNAPP solution can keep up with the pace of change in your organization.
Wiz works across all your cloud providers and data storage solutions. No matter where your data is located, no matter how it’s stored, Wiz has your back, giving your data the highest level of confidentiality, integrity, and availability.
Click here to get a demo and see how simple it is to put Wiz to work—and put your data security worries to rest.
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