Critical national infrastructure (CNI) faces a mounting threat landscape, necessitating a fundamental overhaul of security strategies. Consider last summer’s attack on London hospitals, where just one cyber incident resulted in the postponement of 1,255 planned operations and 3,396 appointments. Incidents like this are unfortunately no longer exceptional. Following the attack, the National

Cyber attacks are getting more common, more serious, and easier to execute. The average Joe (or Jane) can literally go online and pay a company to hack for them, in a transaction known as ransomware as service, or RaaS. AI and machine learning tools can be programmed to hack for their users.

In today’s era, cybersecurity increasingly relies on artificial intelligence (AI) to keep up with evolving threats. As businesses integrate more technology into their processes, the risks of data breaches and cyberattacks also rise. AI-enabled cybersecurity helps identify potential threats, supporting companies in proactively managing risks and meeting regulatory standards. This

As 2024 wraps, the ramifications of the year’s major cybersecurity events show no sign of abating. Record-breaking data breaches affecting companies such as AT&T, Snowflake and Ticketmaster, global IT outages that crippled businesses, and ongoing security staffing shortages are just a few of the trends at play. In addition, geopolitical conflicts and

AI supply chain risks spotlight vulnerabilities in open-source LLMs Recently, DevOps professionals were reminded that the software supply chain is rife with risk, or as I like to say, it’s a raging dumpster fire. Sadly, this risk now includes open source artificial intelligence (AI) software. Especially after further investigations into Hugging Face (think GitHub