Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Offensive AI Lowers the Barrier of Entry for Bot Attackers

The use of artificial intelligence (AI) for defense allows for the better scanning of networks for vulnerabilities, automation, and attack detection based on existing datasets. However this is all in defense against an unknown attacker, who can have varying offensive tools all designed to overcome the most sophisticated defense. Is the biggest challenge for defensive AI that there is an offensive AI operator with unknown capabilities? And has offensive AI lowered the barrier of entry for bot attackers?

The basics of securing GenAI and LLM development

With the rapid adoption of AI-enabled services into production applications, it’s important that organizations are able to secure the AI/ML components coming into their software supply chain. The good news is that even if you don’t have a tool specifically for scanning models themselves, you can still apply the same DevSecOps best practices to securing model development.

The Evolution of Cyber Threats in the Age of AI: Challenges and Responses

Cybersecurity has become a battlefield where defenders and attackers engage in a constant struggle, mirroring the dynamics of traditional warfare. In this modern cyber conflict, the emergence of artificial intelligence (AI) has revolutionized the capabilities of traditionally asymmetric cyber attackers and threats, enabling them to pose challenges akin to those posed by near-peer adversaries.

The Crucial Role of Fall Detection in Modern Medical Alert Systems

As the global population ages, ensuring the safety and well-being of older adults becomes increasingly important. Falls are a major health risk for the elderly, often leading to severe injuries, reduced mobility, and a loss of independence. Fall detection technology, integrated into modern medical alert systems, plays a crucial role in mitigating these risks. This article explores the significance of fall detection, the technology behind it, and its impact on the health and safety of seniors.

How Criminals Are Leveraging AI to Create Convincing Scams

Generative AI tools like ChatGPT and Google Bard are some of the most exciting technologies in the world. They have already begun to revolutionize productivity, supercharge creativity, and make the world a better place. But as with any new technology, generative AI has brought about new risks—or, rather, made old risks worse.

Scaling RAG: Architectural Considerations for Large Models and Knowledge Sources

Retrieval-Augmented Generation (RAG) is a cutting-edge strategy that combines the strengths of retrieval-based and generation-based models. In RAG, the model retrieves relevant documents or information from a vast knowledge base to enhance its response generation capabilities. This hybrid method leverages the power of large language models, like BERT or GPT, to generate coherent and contextually appropriate responses while grounding these responses in concrete, retrieved data.

AI Math Review: An Advanced AI Math Calculator & Solver

In the ever-evolving landscape of educational technology, AI Math emerges as a pioneering solution, designed to demystify the complexities of mathematics for learners across the globe. This comprehensive review delves into the multifaceted features of AI Math, highlighting its capabilities as a photo math solver, a calculator with steps, and an all-encompassing math AI solver. By offering a free, online AI Math problem solver and math calculator, AI Math stands out as a resourceful tool for students, educators, and anyone looking to enhance their mathematical understanding.

The ethical considerations for AI-powered software testing

As AI integrates into every stage of the SDLC, the area of software testing is undergoing transformative and unprecedented changes. In this article, we will discuss the ethical considerations for AI-powered software testing, examining the advantages and potential hurdles generative AI presents as a new technology being applied across the SDLC.

Mitigating Data Poisoning Attacks on Large Language Models

Large language models (LLMs) have experienced a meteoric rise in recent years, revolutionizing natural language processing (NLP) and various applications within artificial intelligence (AI). These models, such as OpenAI's GPT-4 and Google's BERT, are built on deep learning architectures that can process and generate human-like text with remarkable accuracy and coherence.