By Peter Garraghan, Mindgard<\/p>\n
The rapid adoption of AI<\/a> has unleashed a flood of innovation, but it has also exposed a glaring cybersecurity bottleneck of the industry’s own making. Organizations are racing to implement generative models, autonomous agents, and AI-enhanced services, yet too often without any real assurance that these systems are secure. According to a recent World Economic Forum report<\/a>, 66% of businesses expect AI to impact cybersecurity profoundly in the coming year, but only 37% currently assess the security of AI tools before deployment. This is a dangerous paradox: recognition without readiness.<\/p>\n We’ve seen this before. During the rise of cloud computing, security was often an afterthought, until the breaches began. With AI, however, the risks are not only faster and more complex, but also fundamentally different. Treating AI like any other software is a category error. These systems are non-deterministic, probabilistic, and deeply entangled in application workflows. They require a new playbook.<\/p>\n Your AI Can (and Likely Will) Be Hacked<\/p>\n Without sounding too apocalyptic, AI at its core still involves risks related to software, hardware, and data. These are not unfamiliar to security practitioners, who already have controls and processes in place. However, the emergence of a new technological paradigm requires adapting existing tools, training, and playbooks \u2014 and AI is no exception.<\/p>\n