Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Historically, cyber-attacks were labor-intensive, meticulously planned, and needed extensive manual research. However, with the advent of AI, threat actors have harnessed their capabilities to orchestrate attacks with exceptional efficiency and potency. This technological shift enables them to execute more sophisticated, harder-to-detect attacks at scale.

Legacy data leak prevention (DLP) solutions are failing. Simply put, they weren’t built for business environments rooted in SaaS apps and generative AI (GenAI) tools. Meanwhile, security threats are evolving at a breakneck pace, with as many as 95% of enterprises experiencing multiple breaches a year. New attack surfaces are unfurling at a rapid rate following the switch to hybrid and cloud-based workspaces.

With the proliferation of AI/ML enabled technologies to deliver business value, the need to protect data privacy and secure AI/ML applications from security risks is paramount. An AI governance framework model like the NIST AI RMF to enable business innovation and manage risk is just as important as adopting guidelines to secure AI. Responsible AI starts with securing AI by design and securing AI with Zero Trust architecture principles.

A recent report reveals a significant discrepancy in the priorities of mid-market IT departments when it comes to addressing cyber threats. It's somewhat ironic that IT professionals find themselves entangled in a logical paradox when responding to surveys, as demonstrated by Node4’s Mid-Market IT Priorities Report 2024.

The integration of Artificial intelligence (AI) is forcing a significant transformation in the business operations landscape. Through automation, data analysis and predictive capabilities, AI is reshaping how businesses operate as companies look to spur productivity.

In the ever-evolving terrain of artificial intelligence, OpenAI's LangGraph is making waves by introducing a groundbreaking approach to code generation and analysis. With the prominence of tools like GitHub Co-Pilot and the popularity of projects such as GPT-engineer, the demand for innovative solutions in this domain has never been higher. LangGraph aims to meet this demand by leveraging a flow paradigm inspired by recent advancements like AlphaCodium to enhance the efficiency of code generation.

We’ve been talking about digital transformation for years (or even decades?), but the pace of evolution is now being catapulted forward by AI. This rapid change and innovation creates and relies upon exponential data sets. And while technology is rapidly evolving to manage and maintain these massive data sets, legacy pricing models based on data ingest volume are lagging behind, making it economically unsustainable.