Advancements in agentic AI are accelerating, especially in multimodal models that handle text, image and audio inputs together. AI agents are evolving to perform multistep workflows and interact autonomously with external tools and data. This shift is expanding their utility, but it’s also expanding potential risks.
- Attackers are crafting inputs to hijack AI behavior, overriding instructions or extracting sensitive data.
- Bad actors are using agents to engage in phishing, malware development and fraud.
Adversarial testing and red-teaming can help companies address these growing risks by simulating attacks that can uncover vulnerabilities. This is part of a proactive, Responsible AI stance that helps build resilience into AI systems from the start –– and builds trust and drives value.
Without Responsible AI, companies may face real consequences: reputational damage when generative tools surface harmful outputs, operational breakdowns when flawed models disrupt business continuity, systemic bias if training data skews hiring decisions and — in rare, tragic cases — safety incidents that put lives at risk. The right governance approach helps navigate these risks — so you can act with confidence and lead with accountability.
Implementing AI agents demands proper testing and tuning to the role it is meant to fulfill. AI built to act as a customer service representative requires different safety layers than one acting as a financial advisor. Define your AI's role clearly and tailor safeguards to fit the use case.