Top 5 Enterprise Data Security Best Practices

While encryption is undeniably the first step to data security in any organization, it is not a cure-all mechanism. Encryption ensures that data remains unreadable to unauthorized parties, protecting sensitive information wherever it goes.

The next level of data security technology is Confidential Computing, which secures data even while it is processed in computer memory.

Traditional perimeter-based security solutions still account for a substantial part of enterprise security.

However, they fall short, as evidenced by the ongoing reports of data breaches. Encryption provides a highly efficient last line of defense in data security, but controlling encryption at scale is complex, especially across hybrid multicloud environments.

A solid data encryption strategy requires more than robust encryption standards; it demands a comprehensive approach encompassing various best practices. Here are the top five best data security practices to implement alongside solid encryption:

1. Catalog, Classify, & Categorize All Your Enterprise Data

The first step to protecting your data is knowing what you have. You can always find sensitive information with a thorough catalog, making it easier to manage, secure, and comply with regulations. List all your data resources, like databases, files, and cloud storage.

Use advanced software to keep your catalog updated as new data enters, old data changes, or gets deleted. You are likely already relying on cleanup software to remove redundant files, antiviruses for protection, etc. The catalog should not be an exception. Your data catalog must cover the entire infrastructure, especially cloud services, which are often overlooked. Regularly check and verify that your data inventory is complete and accurate.

Identifying and classifying critical data ensures the most important information gets the highest protection level. Classify data based on its importance to your business and regulatory requirements and apply strict security controls to all classified critical data.

By categorizing data by sensitivity and criticality, you can use tailored security measures to ensure the most valuable information is well-protected. Think about categories like public, internal, confidential, and highly confidential.

2. Use Multi-Factor Authentication (MFA)

Implementing Multi-Factor Authentication (MFA) significantly raises the bar for attackers attempting to gain unauthorized access to sensitive data and critical systems. In cloud environments like AWS or Azure, MFA adds an extra security layer beyond the traditional username and password.

Even if an attacker obtains a user's password through phishing or other means, they will still need the secondary authentication factor, such as a temporary code sent to the user's phone or a biometric verification, to gain access. This dual requirement makes it exceedingly difficult for cybercriminals to breach the system.

For example, when MFA is enabled to access admin consoles and critical services in AWS or Azure, it becomes nearly impossible for attackers to misuse stolen credentials.

They would need both the password and access to the secondary verification method, which only the legitimate user possesses. This multi-layered defense greatly reduces the likelihood of successful attacks, thus better protecting sensitive data stored in cloud environments.

3. Deploy a Unified Key Management System

Each cloud vendor offers unique key management solutions for their platforms. This makes it impractical for organizations to implement integrated data security across multiple Cloud Service Providers (CSPs). Instead, organizations end up with multiple proprietary Key Management System (KMS) solutions, leading to key sprawl. There is a high risk of losing track of where keys are, which services use them, if they follow your policy, and so on.

For true Zero Trust security, and to adhere to regulations such as the GDPR, organizations must retain custody of their keys and not implicitly trust CSPs with access to their most sensitive data. They must centrally define and enforce detailed, uniform access control policies.

Turn to Fortanix's unified key management solution, which integrates with key management services environments. Organizations can centrally manage the lifecycle of all keys from a single interface, whether on-premises or in the cloud.

Encryption keys are always under the organization's control and can be stored on highly secure Hardware Security Modules (HSMs) for FIPS 140-2 level 3-type security. This multicloud SaaS key management solution offers Bring Your Own Key (BYOK) and Bring Your Own Key Management Service (BYOKMS), allowing organizations to separate their keys from the data.

4. Use Confidential AI

The potential of AI and data analytics for boosting business growth through data-driven innovation is widely recognized; however, MLOps often need to handle sensitive data like Personally Identifiable Information (PII), which is restricted due to compliance obligations. AI projects can hit a wall if data teams can't use this sensitive information.

Many regulated industries, such as healthcare and BFSI, can't leverage large portions of their data because of privacy concerns. That's where Confidential Computing technology becomes the first choice because it can isolate sensitive data during processing, enabling confidential data collaboration use cases.

Fortanix Confidential AI is designed with the privacy and compliance needs of regulated industries in mind to protect the intellectual property of the AI model. It's a user-friendly software and infrastructure subscription service.

Unlike traditional AI solutions that mainly focus on speeding up modeling, Fortanix Confidential AI helps build richer models and safeguard IP by using secure enclaves for data processing. Most importantly, it provides proof of execution in a trusted environment, which is unavoidable for compliance.

5. Get Ready for Post Quantum

Advances in Quantum Computers are set to make most of our common public key cryptographic algorithms outdated. With certain algorithms, quantum computers will surpass traditional ones in cracking the cryptographic key pairs protecting sensitive data.

That's why organizations need to start thinking about migration strategies for new quantum-proof algorithms and implement them with as little disruption as possible.

The first step is to determine your current cryptographic security posture. Crypto agility is required to transition to a robust and resilient data security setup, with minimal operational disruptions.

Fortanix Key Insight is here to help. It discovers all your cloud encryption keys and data services, assessing and tracking your cryptographic security posture. It shows where your encryption keys are across hybrid multicloud environments, and how well your data services use encryption.

With immediate access to an accurate inventory, Key Insight helps you identify and prioritize where and when to apply post-quantum cryptography.

With Fortanix, organizations can simplify and regain control of their cryptographic operations across multiple clouds, traditional datacenters, and specific regions. This allows them to smoothly transition to new cryptographic standards while efficiently using their resources.

Conclusion

The best data security practices use a layered approach, starting with perimeter security and applying encryption for data-centric security. But relying on encryption alone isn’t enough.

To truly control and monitor your data, you need to know who’s accessing it and how and when they’re doing it. And don’t forget about your encryption keys—where are they stored, and who has access to them? Are there tamper-proof logs in place?

Also, think about the quality of your encryption standards. Are they robust enough to ensure your current data is still secure in the looming post-quantum era?

If not, can you quickly patch any risks? What solutions are out there to help with these problems?

With AI models becoming a more significant part of business operations, it’s inescapable that organizations must eventually build robust AI security plans.