Useful but Risky: A Complicated Relationship Between SMBs and AI Tools

ESET analyzes how SMBs’ rapid adoption of AI tools and agents introduces new attack vectors including misconfigured AI agents, prompt injection attacks, shadow AI, and agents bypassing existing security controls.[4] The article explains that misconfigured agents can move sensitive data or trigger privileged cloud operations, while prompt injection can turn agents into insider-like threats that steal company data or perform unauthorized actions.[4] It recommends SMBs restrict sensitive data in public AI tools, enforce least privilege and MFA for AI access, and choose compliant vendors with clear data usage and training policies to reduce data leakage and AI supply chain risk.[4]

The ESET article reports that SMBs are rapidly adopting AI tools and agents, creating new attack vectors such as misconfigured agents that can move sensitive data or trigger privileged cloud operations, agents that bypass existing security controls like MFA, and prompt injection attacks that turn agents into insider-like threats capable of data theft or unauthorized actions.[1] It also highlights "shadow AI," where employees use unmanaged public AI tools, increasing the risk of data leakage and legal exposure.[1] From a RealGround perspective, these patterns indicate systemic AI agent abuse risks: organizations need secure agent design, least-privilege business logic, continuous adversarial testing, and vendor/SaaS supply chain review to prevent agents from becoming unmonitored high-privilege executors. Practically, SMBs should restrict sensitive data use in public models, enforce strong identity and access controls around agents, and adopt formal AI security readiness and governance programs before scaling AI-assisted workflows.