Responsible AI at Scale: Securing Agentic AI for Critical Infrastructure

Impact at a Glance 

The goal was to create a secure, resilient, and future-ready Agentic AI platform tailored for a European Infrastructure company. The framework mitigates advanced AI-specific threats and embeds Responsible AI principles, GDPR safeguards, and robust governance for AI agents acting on behalf of customers. It is following the EU AI Act and anticipates obligations under the upcoming Cyber Resilience Act (CRA), ensuring long-term compliance and trust. By integrating security-by-design throughout the lifecycle, we deliver transparent, safe, and reliable AI services. This approach enables clients to confidently deploy AI at scale while protecting users, data, and operations. 

 

Initial Situation & Challenge 

A European infrastructure company, in collaboration with a leading cloud platform and building on their enterprise AI stack, aimed to revolutionize its customer service by deploying a next-generation Agentic AI. Unlike simple Generative AI (GenAI), designed for creating text, images or other forms of output, Large, this AI was envisioned to be an “agent” – able to handle tasks ranging from issuing customer support tickets to creating invoices. While the expected business benefits were immense, the leadership team recognized that an AI agent with the power to act introduces a new and complex risk landscape. Misuse or malfunction could lead to unauthorized account changes, accidental data exposure, or regulatory non-compliance – threats that could directly impact customer trust and brand reputation. The initial push for rapid innovation had to be balanced with a robust security strategy to prevent data leaks, compliance failures, and brand damage from the start. 

 

What Was at Stake 

Without a dedicated security framework for Agentic AI in place, the company faced significant and escalating risks that could undermine the entire initiative: 

• Regulatory Exposure: Agents accessing and modifying personal data creates a high risk of severe GDPR violations if sensitive information is leaked. Customers may see their private information exposed, leading to a direct loss of trust and revenue. In this scenario, the CISO will be held accountable for safeguarding data and ensuring compliance, making the risk both personal and organizational. 
• Operational & Financial Damage: A compromised agent could be manipulated to perform unauthorized actions, such as issuing false refunds, directly impacting customer satisfaction and operational efficiency – a key concern for the Head of Customer Experience. 
• Reputational Catastrophe: An Agentic AI system could be manipulated into executing biased, inappropriate, or harmful actions – such as mishandling customer data or applying unfair business logic – which could trigger public backlash and severely damage the company’s brand integrity. Corporate communications and executive leadership would be accountable for addressing the fallout and restoring trust. 
• Business Threat: A significant security incident involving the AI agent could not only halt the project but also lead to investors and partners questioning the company’s ability to innovate safely and therefore threatening its competitive position in the market. 

 

Our Approach: How We Tackled It 

We implemented a secure by design framework that addressed the unique threats of Agentic AI by embedding security into the model, the agent’s tools, and the surrounding infrastructure. The approach was transformational, not just technical: 

• Agent AI – Centric Threat Modeling: The engagement began with a threat model focused specifically on the risks of an AI agent that can take actions. This went beyond standard LLM risks analyzing how the agent’s tools and connected APIs could be abused. 
• By Implementing Multi-Layered AI Defenses we architected a four-pronged defense system: 
• Input/Output Guardrails: A sophisticated filtering layer was implemented to detect and block malicious inputs like prompt injection. This ensures that the AI cannot be tricked into behaving incorrectly or carrying out actions it was not intended to perform. 
• Granular Control over Agent’s Actions: This was the core of agentic security. Every tool the agent could use (e.g., accessing a CRM, interacting with a billing system) was secured following the principle of least privilege. The agent was granted narrow, revocable permissions, and all its actions are continuously logged and monitored for anomalies. 
• Data Minimization and Governance: In line with Responsible AI principles, a strict data governance model was established. The agent was designed to only access the absolute minimum data necessary to complete a task, and the training data was rigorously vetted to mitigate bias. 
• Observability & Monitoring: To ensure transparency and trust, a dedicated observability layer was added. This provided end-to-end visibility into agent decisions, tool invocations, and data flows. Logs were correlated with SIEM/SOC systems, enabling anomaly detection, forensic analysis, and real-time alerts. This layer not only strengthened security but also improved explainability and audit readiness. 
• Continuous Agentic AI-Penetration Testing: We conducted regular, specialized penetration tests to simulate real-world attacks. These tests probed for LLM vulnerabilities like prompt injection, data exfiltration, and model inversion, ensuring the agent’s defenses remained robust against evolving threats. 

 

Measurable Results from the Partnership 

The engagement delivered a secure and trusted foundation for the company’s strategic investment in Agentic AI, with the partnership being extended to govern future AI initiatives: 

• Aligned with Responsible AI & GDPR: The framework provided the technical controls and governance necessary to meet GDPR obligations and CRA principles, passing internal audits with no major findings. 
• Demonstrated Threat Prevention: In the first month of operation, the input/output guardrails blocked more than 1,000 adversarial attempts, including prompt injection and tool exploitation attacks. 
• Enabled Secure Innovation: The security framework gave the company the confidence to roll out the AI agent to its full customer base, improving customer issue resolution time by 30% with no reported security incidents during the launch phase. 
• Established a Reusable AI Security Framework: The multi-layered defense model has been adopted as the standard for all future Agentic AI projects, enabling faster, more secure innovation across the organization. 
• Built In-House AI Security Expertise: The client’s teams developed the know-how to manage LLM and Agentic AI risks, embedding secure AI practices into daily operations. 

This framework is now being extended to other AI initiatives across industries – from banking to healthcare – helping enterprises deploy Agentic AI with confidence and turn a complex risk into a competitive advantage.