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Data Security

The latest News and Information on Data Security including privacy, protection, and encryption.

What is Policy Orchestration?

As an organization’s data volume grows, so does the need to manage its access and governance consistently and securely. The proliferation of vast and siloed data spread across diverse applications, multi-cloud and hybrid environments, and formats over the last few decades has made it difficult to control data access and governance. In order for structured data to be effectively utilized, it needs to be combined and enhanced in a way that supports both operational and analytical tasks.

The Definitive Guide to Endpoint Data Loss Prevention

Backup and recovery solutions, anti-malware tools, data encryption tools, and network security tools—how much protection is really enough to prevent endpoint data loss? In this article, we’ll go over everything you need to know about endpoint data loss prevention, including the types of DLP, specific activities you can monitor, how endpoint DLP software can help, and more.

Personal vs Sensitive Personal Information (SPI): What's the Difference

What is there to know about a person? Certainly, their name, but how about their affiliations, philosophical beliefs, or sexual orientation? The nuanced information about a person—including those elements listed above and more—falls into a data category called “personal information” or “personally identifying information” (PII).

10 Data Security Best Practices: Simple Methods to Protect Your Data

Data security is more important than ever because of an expanding cyber attack surface, frequent supply chain attacks, and evolving AI threats. The global average cost of a data breach reached an all-time high of $4.45 million in 2023, according to the 2023 Cost of a Data Breach Report by IBM Security. In this article, you’ll discover the main data security strategies and 10 methods of protecting data that apply to most industries.

Data Exfiltration: Risks, Detection & Prevention Strategies

Companies today face a wide range of potential threats to digital security. From cyber attacks with malicious intent to internal threats from negligent employees, IT and security teams face remarkable challenges in the modern enterprise environment. Add to the equation that many companies now operate under a hybrid model in which some employees may use personal devices for work purposes, and it’s exceedingly complicated to establish ironclad security policies and incident response plans.
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Cloud computing - The first step to an effective data modernisation strategy

Many organisations realise the benefits that lie within the data they collect daily. This modern approach to data empowers businesses to leverage data for innovation and monetization while at the same time enhancing security and privacy. While this may seem like a conundrum as companies have traditionally buried their data deep into IT architecture to prevent it from being accessible, even by the employees, today it is possible to have the best of both. This process starts with the cloud.

Securing AI with Least Privilege

In the rapidly evolving AI landscape, the principle of least privilege is a crucial security and compliance consideration. Least privilege dictates that any entity—user or system—should have only the minimum level of access permissions necessary to perform its intended functions. This principle is especially vital when it comes to AI models, as it applies to both the training and inference phases.

How Product Security Helps To Protect Your Data at Egnyte

One of the critical challenges for organizations today is securing their data. Organizations must incur the cost associated with proper design, development, and maintenance of systems and their appropriate safeguarding and monitoring. One way to reduce and optimize these costs is to choose tools that operate in a Software-as-a-Service model. Choosing tools in such a model helps shift some of the responsibilities and costs to the vendor.

Firewalls for AI: The Essential Guide

As the adoption of AI models, particularly large language models (LLMs), continues to accelerate, enterprises are growing increasingly concerned about implementing proper security measures to protect these systems. Integrating LLMs into internet-connected applications exposes new attack surfaces that malicious actors could potentially exploit.