Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

San Francisco, CA - Protecto, a leading innovator in data privacy and security solutions, is proud to announce the release of new capabilities designed to identify and cleanse Protected Health Information (PHI) data from structured and unstructured datasets, facilitating the creation of safe and compliant data for Generative AI (GenAI) applications. This advancement underscores Protecto's commitment to data security and compliance while empowering organizations to harness the full potential of GenAI.

When it comes to protecting personally identifiable information (PII), organizations have two main options: pseudonymization and anonymization. Both methods aim to prevent unauthorized disclosure of sensitive PII data, but they differ in their implementation, advantages, and regulatory implications. In this blog, we’ll explore the key differences between pseudonymization vs anonymization, their benefits, practical examples, and how to choose the best method for your organization’s needs.

There’s been a lot of excitement around generative AI technology over the past few years, especially in software development. Developers of all levels are turning to AI tools, such as GitHub Copilot, Amazon CodeWhisperer, and OpenAI’s ChatGPT, to support their coding efforts. In fact, GitHub found that 92% of developers use AI coding tools. However, many businesses are realizing that they need to be more cautious when using AI in software development.

Explore Our Latest Insights on Artificial Intelligence (AI). Learn More. It is easy to assume that large language models (LLMs) and generative AI (GenAI) security products are a mysterious box of magic. While, in general, interactions with these models are abstract; you make an API call to a remote endpoint and receive a response without much exposure to the security controls around the model, there are security risks of AI to consider when using them.

The Evolution of Email Threats Email has long been a favored vector for cyber attacks. From the early days of simple phishing scams to the more advanced spear-phishing campaigns, email threats have consistently evolved. However, the integration of AI has brought about a paradigm shift in both the complexity and frequency of these attacks. AI-Powered Phishing Phishing attacks have traditionally relied on mass-distribution strategies, hoping to catch a small percentage of victims.

ChatGPT impresses everyone with its writing capabilities; however, its proficiency in understanding and generating human-like text has inadvertently empowered threat actors to produce realistic and error-free phishing emails, which can be challenging to detect. The use of ChatGPT in cyberattacks poses a significant threat, particularly for attackers whose first language isn’t English. This tool helps them overcome language barriers, enabling the creation of more convincing phishing content.