Rethinking Security For AI Systems

InnovationRethinking Security For AI SystemsByMichelle Drolet,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 08, 2026, 06:15am EDTMichelle Drolet is CEO of Towerwall, a specialized cybersecurity firm focused on proactive cyber preparedness and compliance services. getty​Cybersecurity has generally kept pace with the growing sophistication of threats. It has evolved from firewall and antivirus to next-gen firewalls, comprehensive endpoint security suites and intelligence-driven approaches such as EDR, XDR and MDR—solutions based on the assumption that systems behave predictably and that risks can be mapped, monitored and mitigated within specific guardrails.But even the best security was not designed for systems that think.AI spending in 2026 is expected to reach $2.5 trillion. This adoption of AI is disrupting the cybersecurity space. We are witnessing rapid AI development with systems powered by large language models (LLMs) and autonomous agents not only executing instructions but also interpreting, generating and making decisions. While there is little doubt that these systems are beneficial, they are also equally difficult to secure.Not Just New Threats, But A Security ResetTraditional security systems, like firewalls, endpoint, EDR and SIEM platforms, identify patterns and signatures and rely on known behaviors to target abnormalities. They are built to observe as many signals as they can, to detect and control risk.But AI doesn’t follow these rules; the attacks levelled at AI environments reflect this fact. A data poisoning attack targets the training process of an AI system by manipulating input data, ultimately shaping what the model learns. Prompt injection attacks alter how the LLM interprets instructions and can manipulate its behavior.Couple these attacks with the complex dynamics introduced into the envi...