Three Essentials for Agentic AI Security
As AI agents travel between systems and platforms, advancing business workflows, they also open vulnerabilities. Learn from one company’s experience addressing agentic AI security risks.
Matt Chinworth
AI agents promise increased productivity by working autonomously across multiple systems — but this very capability can create serious security vulnerabilities. Most companies remain unprepared: Just 42% balance AI development with appropriate security investments. Learn how one company improved agentic AI security using a three-phase framework that included threat modeling, security testing, and runtime protections.
What if your productive new digital employee was also your greatest vulnerability? AI agents — powered by large language models (LLMs) — are no longer futuristic concepts. Agentic AI tools are working alongside humans, automating workflows, making decisions, and helping teams achieve strategic outcomes across businesses. But AI agents also introduce new risks that, if left unmanaged, could compromise your company’s resilience, data integrity, and regulatory compliance.
Unlike older AI applications that operate within narrowly defined boundaries, like chatbots, search assistants, or recommendation engines, AI agents are designed for autonomy.
Among companies achieving enterprise-level value from AI, those posting strong financial performance and operational efficiency are 4.5 times more likely to have invested in agentic architectures, according to Accenture’s quarterly Pulse of Change surveys fielded from October to December 2024. (This research included 3,450 C-suite leaders and 3,000 non-C-suite employees from organizations with revenues greater than $500 million, in 22 industries and 20 countries.) These businesses are no longer experimenting with AI agents; they are scaling the work. But with greater autonomy comes a heightened need for trust — and trust cannot be assumed.
AI agents operate in dynamic, interconnected technology environments. They engage with application programming interfaces (APIs), access a company’s core data systems, and traverse cloud and legacy infrastructure and third-party platforms. An AI agent’s ability to act independently is an asset only if companies are confident that those actions will be secure, compliant, and aligned with business intent.
Yet, most companies are not ready for AI security risks. Only 42% of executives surveyed said they are balancing AI development with appropriate security investments. Just 37% have processes in place to assess the security of AI tools before deployment.
How can leaders bridge this preparedness gap? The experience of a leading Brazilian health care company illustrates three best practices for agentic AI security.
Agentic AI Security: A Three-Phase Framework
The Brazilian health care provider has more than 27,000 employees in over a dozen states and offers a wide range of medical services, including laboratory tests, imaging exams, and treatments, across various specialties. The company set out to eliminate a costly bottleneck: manually processing patient exam requests. The task — transcribing data from paper forms and getting it into internal systems — was labor intensive, slow, and prone to human error.