Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Healthcare data masking unlocks the incredible potential of healthcare data for analytics and AI applications. The insights from healthcare data can revolutionize the industry from improving patient care to streamlining operations. However, the use of such data is fraught with risk. In the United States, Protected Health Information (PHI) is regulated by the Health Insurance Portability and Accountability Act (HIPAA), which sets stringent requirements to safeguard patient privacy.

Data protection in healthcare ensures patient information remains secure. The rise of digital health records increases the risks of data breaches and cyber threats. Strong security measures are essential to prevent unauthorized access and misuse of sensitive information. Healthcare institutions must implement advanced security protocols to combat evolving threats and ensure patient trust.

The term LLMjacking refers to attackers using stolen cloud credentials to gain unauthorized access to cloud-based large language models (LLMs), such as OpenAI’s GPT or Anthropic Claude. This blog shows how to strengthen LLMs with Sysdig. The attack works by criminals exploiting stolen credentials or cloud misconfigurations to gain access to expensive artificial intelligence (AI) models in the cloud. Once they gain access, they can run costly AI models at the victim’s expense.

Cybersecurity involves anticipating threats and designing adaptive strategies in a constantly changing environment. In 2024, organizations faced complex challenges due to technological advances and sophisticated threats, requiring them to constantly review their approach. For 2025, it is crucial to identify key factors that will enable organizations to strengthen their defenses and consolidate their resilience in the face of a dynamic and risk-filled digital landscape.

LLMs are based on neural network architectures, with transformers being the dominant framework. Introduced in 2017, transformers use mechanisms called attention mechanisms to understand the relationships between words or tokens in text, making them highly effective at understanding and generating coherent language. Practical Example: GPT (Generative Pre-trained Transformer) models like GPT-4 are structured with billions of parameters that determine how the model processes and generates language.

Cybersecurity researchers are warning about a new breed of investment scam that combines AI-powered video testimonials, social media malvertising, and phishing tactics to steal money and personal data. Known as Nomani — a play on "no money" — this scam grew by over 335% in H2 2024, with more than 100 new URLs detected daily between May and November, according to ESET's H2 2024 Threat Report.