What is DSPM? Data Security Posture Management, or DSPM refers to the practice of assessing and managing an organization’s overall data security posture. It involves monitoring, evaluating, and continuously improving the effectiveness of data security controls and measures in place to protect sensitive information. What is Data Security Posture Management? It provides a holistic view of an organization’s data security status and helps identify vulnerabilities, gaps, and areas for improvement.

While today’s AI is focused on user-agent interactions, the real revolution is on the horizon, where AI agents will increasingly communicate, collaborate, and share data amongst agents. This shift brings unprecedented opportunities and new and complex data protection challenges.

Artificial Intelligence (AI) plays a critical role in modern data handling. AI processes vast amounts of data, from personal information to business analytics, at unprecedented speeds. This raises serious concerns about AI and data protection. With AI’s growing capabilities, ensuring the security of personal data is essential. The AI Act aims to regulate AI systems, focusing on responsible data usage. It introduces rules that safeguard user data, complementing existing regulations like GDPR.

Artificial Intelligence (AI) has evolved into a vital part of modern businesses. Its reliance on large amounts of data drives efficiency and innovation. However, the need for data security in AI systems has grown critical with this increasing dependence on AI. Sensitive data used in AI must be protected to avoid breaches and misuse. This post will explore critical threats to AI data security, discuss mitigation techniques, and present best practices to help organizations safeguard their AI systems.

As businesses increasingly adopt Generative AI (Gen AI) to enhance operations, customer engagement, and innovation, the need for robust AI guardrails has never been more critical. While Gen AI offers transformative potential, it also introduces significant risks that can jeopardize your business if not properly managed. Below, we explore five critical risks associated with Gen AI and provide strategies to avoid them.

AI guardrails are vital for ensuring the safe and responsible use of AI/large language models (LLMs). However, focusing solely on single prompt-level checks can leave organizations vulnerable to sophisticated threats. Many company policy violations and security risks can be cleverly split across multiple, seemingly innocent queries. To effectively protect against these threats, a more comprehensive approach is needed — session-level monitoring.

AI in data security transforms how organizations protect sensitive information. Companies turn to artificial intelligence for robust defense mechanisms as cyber threats evolve. This cutting-edge technology analyzes vast datasets, identifies patterns, and responds to threats in real-time, surpassing human capabilities. From small businesses to large enterprises, AI-powered solutions guard against increasingly sophisticated attacks.

Personally Identifiable Information (PII) refers to any data that can identify an individual. In today’s digital world, protecting PII is crucial. As data breaches become more common, businesses must protect their sensitive information. PII masking plays a vital role in data security. It involves altering or hiding specific data elements to prevent unauthorized access. This practice is essential for companies that handle large volumes of customer data.

In today’s digital world, protecting sensitive data is more critical than ever. Organizations handle vast amounts оf information daily, much оf which includes sensitive data like Personally Identifiable Information (PII), financial details, and confidential business records. The exposure of this data can lead to severe consequences, including identity theft, financial loss, and reputational damage.

Unlike traditional software, where testing is relatively straightforward, Gen AI apps introduce complexities that make testing far more intricate. This blog explores why traditional software testing methodologies fall short for Gen AI applications and highlights the unique challenges posed by these advanced technologies.