Agentic AI Security Compliance
What if your most productive digital employee also posed your greatest vulnerability?
This is the paradox many companies face with the rise of agentic AI tools. No longer just futuristic concepts, these AI agents, powered by large language models (LLMs), are now essential parts of modern business operations.
They automate workflows, make decisions, and help teams achieve strategic outcomes. However, with their increasing autonomy comes a new array of risks that, if not managed effectively, could compromise a company’s resilience, data integrity, and regulatory compliance. Unlike traditional AI applications—such as chatbots, search assistants, or recommendation engines—AI agents are designed for autonomy, including agentic AI applications, particularly in AI security in the context of large language models, including AI security applications.
This autonomy is an asset, but only if companies can ensure the actions of these agents are secure, compliant, and aligned with business objectives. Accenture’s quarterly Pulse of Change surveys from late 2024 reveal that businesses posting strong financial performance and operational efficiency are 4.5 times more likely to have invested in such architectures.
However, most companies are not yet prepared for the security risks that accompany these advancements (Accenture, 2024), including agentic AI applications, including AI security applications in the context of large language models. AI agents operate in dynamic, interconnected technology environments, engaging with application programming interfaces (APIs), accessing core data systems, and traversing both cloud and legacy infrastructures. This complexity introduces potential vulnerabilities, and only 42% of executives surveyed say they balance AI development with appropriate security investments (Unknown).
So, how can leaders bridge this preparedness gap?
AI security in healthcare efficiency
The experience of a leading Brazilian healthcare company provides a valuable case study in managing the security challenges posed by agentic AI. Facing a costly bottleneck of manually processing patient exam requests, the company turned to AI tools to improve efficiency and accuracy.
These tools used optical character recognition (OCR) and LLMs to process data from scanned forms, routing it accurately across multiple platforms without human intervention, particularly in large language models. However, this efficiency came with increased security risks. In March 2024, the company restructured its AI security architecture in three phases: threat modeling, stress-testing, and enforcing real-time safeguards.
This structured approach enabled them to identify and mitigate vulnerabilities effectively, ensuring their AI systems could operate securely and compliantly.
security vulnerabilities threat modeling
To understand and address potential security gaps, the healthcare company conducted comprehensive threat modeling and enterprise-wide integration mapping. This process cataloged interactions between LLM components, human operators, and other systems.
Using the Open Web Application Security Project’s Top 10 for LLM Applications framework, the company identified critical vulnerabilities, such as data poisoning and prompt injection. Data poisoning is a significant threat, as it involves manipulating training data to degrade system integrity and performance, particularly in agentic AI, particularly in AI security, including large language models applications. In fact, 57% of organizations in a recent survey expressed concern about this issue (Accenture, 2024).
The company discovered that malicious actors could insert misleading examples into its training stream, distorting the AI’s judgment. Prompt injection, another identified threat, occurs when malicious instructions are embedded in seemingly benign content, potentially hijacking system behavior, including agentic AI applications, particularly in AI security, particularly in large language models.
Together, these vulnerabilities posed significant threats to patient safety and risked compliance breaches. By identifying these risks early, the company could take proactive measures to safeguard its systems.
adversarial testing AI vulnerabilities
To confront these identified risks, the company incorporated adversarial testing into every phase of its AI development life cycle. This included red-teaming exercises—controlled attacks designed to expose vulnerabilities before malicious actors could exploit them.
For example, engineers created a scenario using a scanned medical form with a hidden prompt embedded at the bottom. The AI, trained to process form data, interpreted this malicious instruction, revealing how easily a well-crafted prompt could manipulate outcomes in the context of agentic AI, particularly in AI security in the context of large language models, including agentic AI applications in the context of AI security in the context of large language models. This exercise provided a blueprint for enhancing security measures.
Beyond technical defenses, the company institutionalized AI-specific failure protocols. Cross-functional teams conducted simulations of AI-triggered disruptions, rehearsing response actions like system isolation and root cause analysis.
These drills prepared the teams to respond quickly, contain impact, and maintain operational continuity.
AI security runtime protections
In the final phase, the company focused on enforcing stringent runtime protections. This involved improving system guardrails to prevent prompt-injection attempts and validating inputs from OCR-processed images.
By conducting integrity checks of training data and building AI-specific security into every interaction point, they significantly reduced the potential for unauthorized use or data leaks. Strict access controls ensured both AI and human users operated with only necessary permissions, and data was fully encrypted, particularly in agentic AI, particularly in AI security, including large language models applications. These measures also addressed risks associated with shadow AI, where employees might use unsanctioned AI tools at work.
By mapping every interaction, the company could expose hidden data connections, highlight critical controls, and improve anomaly detection. For the healthcare company, these efforts led to a marked reduction in cyber vulnerabilities within its AI ecosystem, especially regarding agentic AI, particularly in AI security in the context of large language models.
The company now operates with greater confidence, scaling AI agents across more workflows while being familiar with vulnerabilities and risk mitigation strategies.
AI security safeguards innovation
For CEOs and their teams, the message is clear: to scale agentic AI with confidence, leaders must think beyond compliance. Mapping vulnerabilities across an organization’s tech ecosystem, simulating real-world attacks, and embedding safeguards that protect data and detect misuse in real-time are essential steps.
These efforts support not just defense but also resilient and scalable AI innovation, especially regarding AI security, particularly in large language models. By adopting a structured, phased approach like the Brazilian healthcare company, businesses can harness the power of AI agents while safeguarding their operations and maintaining public trust. As AI continues to evolve, staying ahead of potential threats will be crucial for securing long-term success.
