It's now possible for artificial intelligence (AI) programming systems to create false information and present it as fact – and even trick cybersecurity experts into thinking the information is true.
Cyber Security
Cyber criminals, state-sponsored hackers and even the occasional disgruntled employee are constantly looking to gain unauthorized access for a variety of purposes: theft of money, cyber espionage, personal information for sale or for use in scams, and damage to critical infrastructure for just a few of the most common.
So how does an organization mitigate an entire world full of continual cyber attacks? Just as buildings have a number of necessary elements of physical security: access control, cameras, alarms and so on; there are similar key elements of cyber security that are absolutely vital for just about any modern business.
It starts with identifying and closing the most common doors that attackers use. For example, phishing attacks on employees are far and away the most common initial point of entry. The breach of even a low-level employee account can quickly turn into an escalation in access privileges and the ability to reach sensitive information. This is also true of smart devices, which are generally more poorly secured than computers and phones.
Most of the conversation around AI in cybersecurity focuses on how attacks are getting faster and more sophisticated. That is true, but it misses a more immediate issue. Many security teams are still operating in ways that assume a much slower threat environment.
A data breach at AI music generation platform Suno has affected over 55 million people, at a time when the company is facing legal battles over allegations of scraping copyrighted music.
Boards are starting to ask the right question about AI risk. Unfortunately, many organizations still don’t have a credible answer.
File-based malware has long been among the most effective attack vectors employed by threat actors worldwide. While AI-powered detection technologies are coming to market to help address these growing risks, their outputs should be complemented by deterministic controls and human oversight, particularly in high-consequence environments.
AI agents will change how SOCs work, but they won’t save a broken data foundation. If your telemetry is siloed, your schemas are inconsistent, or your context is missing, you’ll automate noise, not insight.
A critical vulnerability discovered by AI spans most of the history of NGINX, which was first made available in 2004. The web server is frequently used as a load balancer and cache for static content, and some recent estimates find that about 20 to 30% of the world's busiest websites make use of it.
Artificial intelligence (AI) has rapidly emerged as the double-edged sword of the cyber threat environment. Sophisticated AI models now serve as both potent tools for attackers and vulnerable hinge points for organizations girding against intrusions.
As we enter 2026, AI-native automation is fundamentally reshaping telemetry pipeline management. As a result, around 80% of configuration tasks currently hand-built by Observability/Security teams will be automated, transforming the roles of those teams from builders to strategic drivers.
The IT security industry is facing a new wave of hacking through smart technology, machine learning, and artificial intelligence where hackers will unleash unethical tactics to target and manipulate individuals and organizations.










