In artificial intelligence (AI), especially within natural language processing (NLP), tokenization is a fundamental process that breaks down text into smaller, manageable units known as tokens. Depending on the specific task and model, these tokens can be individual words, subwords, characters, or even symbols.

Data sharing for offshore testing, development, and other operational needs is often essential in the healthcare industry. Yet, laws governing Protected Health Information (PHI) make this challenging, as sending sensitive data outside the U.S. can introduce significant regulatory risks. To stay compliant, healthcare companies need solutions that can anonymize data without compromising its usability or accuracy.

APIs are the backbone of modern applications, enabling seamless data exchange between systems. However, the rise of AI agents fundamentally shifts how APIs are utilized. Regular APIs, originally built for deterministic, non-AI use cases, are not inherently designed to handle the complexities and unpredictability of AI-driven applications. Using your regular APIs directly for AI agents or allowing AI agents to integrate without safeguards exposes your systems and data to significant risks.

Data tokenization is a critical technique for securing sensitive information by substituting it with non-sensitive tokens. This process plays a crucial role in data protection, especially in industries handling large volumes of personal or financial information. Here, we explore the top data tokenization tools of 2024 to help organizations find the right solutions for protecting their data.

As organizations increasingly rely on cloud data platforms, securing PII (Personally Identifiable Information) has become more critical than ever. Snowflake, a robust cloud-based data warehouse, stores and processes vast amounts of sensitive information. With the rise in data breaches and stringent regulations like GDPR and CCPA, safeguarding PII data in Snowflake is essential to ensure data privacy and compliance.

The growing reliance on generative AI is transforming industries across the globe. From automating tasks to improving decision-making, the potential of these systems is vast. However, with this progress comes significant risks. Generative AI can be unpredictable, creating new vulnerabilities that expose organizations to data privacy breaches, compliance failures, and other security issues. So, how can companies harness the power of AI while ensuring they remain protected?

As artificial intelligence (AI) grows, AI guardrails ensure safety, accuracy, and ethical use. These guardrails are a set of protocols and best practices designed to mitigate risks associated with AI, such as bias, misinformation, and security threats. They are vital in shaping how AI systems, particularly generative AI, are developed and deployed.

Deploying large language models (LLMs) securely and accurately is crucial in today’s AI deployment landscape. As generative AI technologies evolve, ensuring their safe use is more important than ever. LLM guardrails are essential mechanisms designed to maintain the safety, accuracy, and ethical integrity of these models. They prevent issues like misinformation, bias, and unintended outputs.

The integration of AI, especially Gen AI, into healthcare has been transforming the industry, enabling providers to enhance patient care, streamline operations, and reduce costs. Below is an overview of the most promising AI use cases in healthcare that are reshaping the industry.

Data protection has become a critical concern worldwide as digital transactions and data exchanges grow. Countries are establishing strict data protection laws to safeguard personal information, and India is no exception. The Digital Personal Data Protection (DPDP) Act is India’s response to growing privacy concerns and the need for robust regulations around personal data usage.