Author: Smit Dagli

The rapid advancement of artificial intelligence (AI) in cybersecurity has been widely celebrated as a technological triumph. However, it's time to confront a less discussed but critical aspect: Is AI becoming more of a liability than an asset in our digital defense strategies? I talk about the unintended consequences of AI in cybersecurity in this essay, challenging the prevailing notion of AI as an unalloyed good.

I’ll start off with the example of deep penetration testing, a critical aspect of cybersecurity that has been utterly transformed by AI. We used to traditionally rely on formulaic methods that were confined to identifying known vulnerabilities and referencing established exploit databases. But AI? It’s changed the game entirely. AI algorithms today are capable of uncovering previously undetectable vulnerabilities by making use of advanced techniques like pattern recognition, machine learning, and anomaly detection. These systems learn from each interaction with the environment and keep adapting continuously. They can intelligently identify and exploit weaknesses that traditional methods might overlook. That’s an improvement, right?

The evolution of natural language processing with Large Language Models (LLMs) like ChatGPT and GPT-4 marks a significant milestone, with these models demonstrating near-human comprehension in text-based tasks. Moving beyond this, OpenAI's introduction of Large Multimodal Models (LMMs) represents a notable shift, enabling these models to process both images and textual data. This article will focus on the core text interpretation techniques of LLMs — tokenization and embedding — and their adaptation in multimodal contexts, signaling an AI future that transcends text to encompass a broader range of sensory inputs.

Turning raw text into a format that Large Language Models like GPT-4 can understand involves a series of complex and connected steps. Each of these processes — tokenization, token embeddings, and the use of transformer architecture — plays a critical role in how these models understand and generate human language.

When managing a database, ensuring data availability and integrity is paramount, especially in the face of hardware failures, software bugs, or natural disasters. SQL Server offers a suite of features designed to provide high availability (HA) and disaster recovery (DR) solutions. This article delves into the technical aspects of SQL Server's HA and DR options. These include always-on availability groups, database mirroring, log shipping, and replication. We'll also cover strategies for implementing a disaster recovery plan through a simulation/example.

Always-On Availability Groups

Always-on availability groups (henceforth referred to as AGs in this article) in SQL Server mark a significant advancement in database technology, enhancing the landscape of high availability (HA), disaster recovery (DR), and read-scale capabilities beyond what was previously achievable with database mirroring. Introduced in SQL Server 2012, AGs offer a sophisticated, scalable solution designed to ensure continuous data availability and system resilience across a range of scenarios.