Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As businesses increasingly rely on digital infrastructures, the threats that aim to exploit these technologies also evolve. It’s no longer just about safeguarding against unauthorized access; it’s about understanding and mitigating the complex risks introduced by AI and machine learning—topics I’ve often discussed, emphasizing the need for an advanced cybersecurity strategy that evolves as quickly as the technologies it aims to protect.

Encryption is a fundamental procedure in cybersecurity that transforms data into a coded format, making it inaccessible to unauthorized users. It has evolved significantly from simple ciphers in ancient times to complex algorithms like AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman), which are used today. Encryption ensures data confidentiality, integrity, and authenticity, which is crucial in protecting sensitive information across various domains.

Russian state-sponsored media organization RT has been using AI-powered software to generate realistic social media personas and spread disinformation for the past two years. This sophisticated tool, known as Meliorator, has been employed to target multiple countries, including the US, Poland, Germany, the Netherlands, Spain, Ukraine, and Israel. Meliorator's Capabilities.

Opinions regarding artificial intelligence (AI) range from fears of Skynet taking over to hope regarding medical advancements enabled by AI models. Regardless of where you sit on this spectrum of anxiety and hype, it is evident that the AI epoch is upon us.

Monitoring and auditing are critical components of cybersecurity, designed to detect and prevent malicious activities. Monitoring involves real-time observation of system activities, while auditing entails a systematic review of logs and interactions. Large Language Models (LLMs), such as GPT-4, are increasingly integrated into various applications, making them attractive targets for cyber threats.

The National Institute of Standards and Technology (NIST) has announced the launch of Assessing Risks and Impacts of AI (ARIA), a groundbreaking evaluation program to guarantee the secure and trustworthy deployment of artificial intelligence. Spearheaded by Reva Schwartz, ARIA is designed to integrate human interaction into AI evaluation, covering three crucial levels: model testing, red-teaming, and field testing.

API Management is a comprehensive process that involves creating, publishing, documenting, and overseeing application programming interfaces (APIs) in a secure, scalable environment. APIs are the backbone of modern software architecture, enabling interoperability and seamless functionality across diverse applications. They facilitate the integration of different software components, allowing them to intercommunicate and share data efficiently.

The rapid rise of low-code and no-code platforms has democratized application development, enabling even non-technical business users to swiftly create critical business applications. However, this accessibility brings new security challenges, particularly with the integration of AI technologies such as copilots, which are used to automate tasks and enhance functionality within these platforms. Zenity enhances the security of these AI-integrated environments by managing and securing AI copilots.

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.