Artificial intelligence is rapidly becoming embedded in everyday business operations. From customer support chatbots and analytics tools to development assistants and workflow automation, organizations are increasingly relying on AI to move faster and operate more efficiently. But according to the 2026 Thales Data Threat Report, AI is also creating a new category of cybersecurity risk: the machine insider.
Traditionally, insider threats meant employees, contractors, or partners who had legitimate access to internal systems. Now, organizations must consider that the same kind of trusted access is being granted to AI systems and that shift is creating a new security challenge.
Many businesses deploy AI tools to improve productivity, automate workflows, and analyze large datasets. To function effectively, these systems often need broad access to internal data and systems. The problem is that organizations frequently treat AI as a helpful tool rather than as a privileged identity within their infrastructure.
According to recent research:
In other words, companies may spend years tightening employee access controls while giving AI platforms broad permissions to sensitive systems. If those controls are weak, AI can amplify the problem at machine speed.
Another challenge highlighted in recent research is the lack of visibility many organizations have into their own data environments.
For example:
When AI systems are introduced into these environments, they may interact with data sources that security teams cannot fully monitor or govern. That creates significant risk if an AI model accesses sensitive intellectual property, interacts with customer data, or connects to internal knowledge base. For industries like life sciences, manufacturing, and engineering, AI might even be used to process regulated data. Many cybersecurity programs were designed around the assumption that humans are the primary users inside a system. AI breaks that assumption.
Modern AI tools can work at super-human speeds, accessing thousands of files in seconds, interacting with multiple systems simultaneously, and generating automated outputs faster than a human can review them. This speed and scale means that small misconfigurations or policy mistakes can propagate much faster than in traditional environments. Unfortunately, many organizations are still relying on security models designed for human users. Research shows 53% of companies still rely primarily on traditional security programs that were not built for AI-driven environments.
Not sure where to start? Organizations adopting AI do not have to navigate these challenges alone. Building secure AI environments requires a thoughtful approach that combines governance, visibility into data environments, and modern security frameworks designed for automated systems. With the right strategy in place, businesses can take advantage of AI’s benefits while maintaining strong protections around their data and infrastructure. Contact us to learn more.