Generative AI's positive impact on cybersecurity spending stems from three distinct drivers: it massively expands the digital "surface area" needing protection (more code, more agents), it elevates the threat environment by empowering adversaries, and it introduces new data governance and regulatory challenges.
Unlike human attackers, AI can ingest a company's entire API surface to find and exploit combinations of access patterns that individual, siloed development teams would never notice. This makes it a powerful tool for discovering hidden security holes that arise from a lack of cross-team coordination.
For AI agents, the key vulnerability parallel to LLM hallucinations is impersonation. Malicious agents could pose as legitimate entities to take unauthorized actions, like infiltrating banking systems. This represents a critical, emerging security vector that security teams must anticipate.
For 2026, AI's primary economic effect is fueling demand through massive investment in infrastructure like data centers. The widely expected productivity gains that would lower inflation (the supply-side effect) won't materialize for a few years, creating a short-term inflationary pressure from heightened business spending.
Major tech companies view the AI race as a life-or-death struggle. This 'existential crisis' mindset explains their willingness to spend astronomical sums on infrastructure, prioritizing survival over short-term profitability. Their spending is a defensive moat-building exercise, not just a rational pursuit of new revenue.
Organizations must urgently develop policies for AI agents, which take action on a user's behalf. This is not a future problem. Agents are already being integrated into common business tools like ChatGPT, Microsoft Copilot, and Salesforce, creating new risks that existing generative AI policies do not cover.
Traditional software automated standardized processes but struggled with complex human interactions like call center support. Generative AI's ability to understand natural language allows software to automate these nuanced tasks, dramatically expanding the total addressable market by tackling problems that were previously impossible to solve with code.
Historically, labor costs dwarfed software spending. As AI automates tasks, software budgets will balloon, turning into a primary corporate expense. This forces CFOs to scrutinize software ROI with the same rigor they once applied only to their workforce.
AI 'agents' that can take actions on your computer—clicking links, copying text—create new security vulnerabilities. These tools, even from major labs, are not fully tested and can be exploited to inject malicious code or perform unauthorized actions, requiring vigilance from IT departments.
While sophisticated AI attacks are emerging, the vast majority of breaches will continue to exploit poor security fundamentals. Companies that haven't mastered basics like rotating static credentials are far more vulnerable. Focusing on core identity hygiene is the best way to future-proof against any attack, AI-driven or not.
When companies don't provide sanctioned AI tools, employees turn to unsecured public versions like ChatGPT. This exposes proprietary data like sales playbooks, creating a significant security vulnerability and expanding the company's digital "attack surface."