Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In the age of generative AI, data security is a key concern for organizations to manage. In my previous blog post, I dug into how modern SSE technology helps to better secure genAI. The recently published ebook Securing GenAI for Dummies offers further clarity on strategies organizations can use when it comes to securing and enabling genAI apps. With that in mind, we’ve compiled 10 essential questions to keep in mind as you assess your data security, along with how Netskope can help address them.

When evaluating models or products for their ability to scan and mask Personally Identifiable Information (PII) in your data, it's crucial to follow a systematic approach. Let’s assume you have a dataset with 1,000,000 rows, and you want to scan and mask each row.

Whether organizations are looking to prevent data exposure, meet leading compliance standards, or simply earn customer trust, Data Leak Prevention (DLP) policies are effective tools for pinpointing and protecting sensitive data across the cloud and beyond. DLP policies are especially useful in the following top use cases.

A large solutions and services company facing strict compliance regulations and enforcements needed a powerful, scalable enterprise data protection solution for their data being migrated over to S3, Athena, Amazon Redshift, and Glue environments. The sensitive data included HR, Financial and customer information. Using Protegrity’s field-level data protection, the company overcame this challenge, significantly improving their processes.

Recent high-profile breaches at Ticketmaster and Santander, attributed to the ShinyHunters group, have brought the critical need for robust data protection measures to the forefront. ShinyHunters, a notorious cybercriminal group known for stealing data from various organizations for financial gain, has demonstrated how vulnerabilities in third-party platforms can lead to significant data breaches.

Since its mainstream emergence in 2022, generative AI has triggered a seismic shift in data management and security. It is estimated that one in four employees now uses genAI apps daily, often unbeknownst to their employer and IT team. This raises concerns, as genAI is designed with a voracious appetite for consuming both mundane and sensitive data. Effectively securing your data as genAI becomes prevalent is a strategic imperative.

Nightfall has been named a Leader in Data Loss Prevention (DLP), Sensitive Data Discovery, Data Security, and Cloud Data Security in G2’s Summer ‘24 reports. We’d like to extend a huge thank you to all of Nightfall’s customers and supporters for making this possible. We’re also happy to acknowledge the Nightfall team’s tireless innovation, all in pursuit of helping customers to protect their sensitive data across the sprawling enterprise attack surface.

Human illness is inevitable. So are data breaches. In 2023 alone, there were 10,626 confirmed data breaches, doubling that in 2022 (5,199 breaches). You cannot protect yourself 100% from getting ill. But you can proactively adopt a healthy lifestyle and habits to help reduce the risk and the impact of an illness and recover quickly. Similarly, your organization cannot protect itself 100% from the outcome of a data breach.

In 2013, the Australian Prudential Regulation Authority (APRA) introduced Prudential Practice Guide CPG 235, a comprehensive framework designed to enhance data risk management across the finance sector. This guide provides financial institutions with principles and best practices to safeguard data integrity, confidentiality, and availability. This blog explores CPG 235, its key components, compliance requirements, and how implementing the framework can enhance data security standards at your organization.