Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

This is the third blog in our series, where we embark on a journey of Branch Transformation with the Next Gen SASE Branch solution. Built on the Netskope One SASE platform, the Next Gen SASE Branch solution combines its three layers–Context-aware SASE Fabric, Zero Trust Hybrid Security, and a SkopeAI-powered Cloud Orchestrator–into a unified cloud offering.

Until 2023, India’s data privacy landscape was largely unregulated – businesses didn’t have to worry about how they process and store data. Sensitive customer data like Personally Identifiable Information (PII) could travel around the world in 80 days and land back to its source – without violating a single regulation. While the unregulated digital space was a boon for data dependent businesses, it was a bane for customer privacy.

For most companies today, the question isn’t whether a data breach will occur, but rather when it will occur. This predicament is primarily due to the sheer volume of data, the challenges associated with monitoring sensitive data, and the transition to remote work. Consequently, IT security teams are constantly navigating a dynamic and enduring risk landscape, making it exceptionally challenging to maintain data security and implement effective sensitive data protection strategies.

Enterprise data is a tremendous asset, but did you know it could also cause great data privacy-related financial risks? The need for sturdy enterprise data protection cannot be emphasized enough. With local data privacy laws such as GDPR being strictly enforced by countries worldwide, companies are seeing heftier fines for data breaches. Companies now need to be extremely cautious about how they manage privacy risks by carefully controlling access to personal and sensitive data.

Large Language Models (LLMs) transform how organizations process and analyze vast amounts of data. However, with their increasing capabilities comes heightened concern about LLM security. The OWASP Top 10 for LLMs offers a guideline to address these risks. Originally designed to identify common vulnerabilities in web applications, OWASP has now extended its focus to AI-driven technologies. This is essential as LLMs are prone to unique LLM vulnerabilities that traditional security measures may overlook.

PII (Personally Identifiable Information) refers to data that can directly or indirectly identify an individual, such as names, addresses, or phone numbers. Protecting PII data is critical, as exposure can result in identity theft, financial fraud, or privacy breaches. With businesses collecting vast amounts of PII, proper PII data classification has become essential to safeguarding sensitive information and complying with data protection regulations.