The customer is a large international development and financial institution that operates globally, supporting governments and organizations through policy analysis, financial programs, and data-driven decision-making. As part of its modernization efforts, the institution deployed multiple AI-enabled systems to assist with research, advisory workflows, and internal knowledge access. These systems processed sensitive economic data, internal assessments, and policy-related information that could have serious consequences if exposed or manipulated.

The security team recognized that AI introduced a fundamentally different risk profile. Unlike traditional applications, AI systems could be influenced through language, context, and iterative interaction, making them susceptible to subtle manipulation over time. Existing security controls were designed to prevent known classes of application attacks but did not account for how attackers might probe AI behavior, infer system capabilities, or chain weaknesses across models, prompts, and connected systems.

Initial AI testing efforts focused on static or one-off prompt checks. While these surfaced occasional failures, they did not reflect how real adversaries operate. Attackers do not submit a single malicious input and stop. They explore boundaries, adapt based on responses, and deliberately shape interactions to bypass controls. The organization needed a way to evaluate AI security from the attacker’s perspective and to understand how its AI systems could be abused in realistic threat scenarios.

Attacker-Aligned Reconnaissance and AI Red Teaming with Mindgard

To address this gap, the institution deployed Mindgard to test its AI systems using an explicit attacker’s mindset. Rather than validating AI behavior against predefined expectations, Mindgard emulated how a real adversary would scope, probe, and exploit AI systems over time.

The engagement began with automated reconnaissance to understand what an attacker could learn through interaction. Mindgard analyzed AI behaviors, inferred system capabilities, and identified how the systems responded under varying contextual pressure. This phase revealed how much information could be extracted indirectly, how guardrails influenced behavior, and where inconsistencies could be exploited.

Using this intelligence, Mindgard executed multi-step AI red teaming designed to mirror real attack progression. Testing focused on how an attacker could incrementally bypass safeguards, manipulate system prompts, or coerce AI-driven workflows into unintended states. Rather than testing isolated prompts, Mindgard chained interactions together to reflect how adversaries adapt based on system feedback.

Crucially, Mindgard evaluated the AI systems in their full operational context. This included how AI outputs influenced downstream processes, how internal data sources were accessed, and how policy controls behaved under adversarial pressure. Findings were framed in terms of attacker objectives, showing not just that a weakness existed, but how it could be exploited to achieve specific outcomes.

Realistic Threat Modeling and Stronger Defensive Controls

By adopting an attacker-aligned approach, the organization achieved several meaningful improvements in AI security posture.

First, the security team gained a realistic understanding of how AI systems could be abused. Mindgard uncovered exploitation paths that would not have been identified through static testing, including subtle guardrail bypasses and behavioral manipulation that emerged only through iterative interaction. This shifted internal conversations from hypothetical AI risk to concrete attacker scenarios.

Second, the findings enabled more effective defensive design. Because vulnerabilities were presented in terms of attacker behavior and intent, engineering teams could design controls that directly disrupted realistic attack paths rather than addressing symptoms. This resulted in stronger system prompts, more resilient guardrails, and tighter integration controls.

Third, the organization established a repeatable method for testing AI from an adversarial perspective. As AI systems evolved, Mindgard was used to continuously reassess behavior against attacker techniques, ensuring that new features or data integrations did not introduce unexamined risk.

Finally, the attacker-focused evidence supported governance and oversight requirements. Security leaders were able to demonstrate that AI systems were being tested not just for correctness or safety, but against realistic threat models aligned with how adversaries operate in practice.

For this global institution, Mindgard enabled a shift from defensive assumptions to attacker-informed security assurance. By operating with an attacker’s mindset, the organization strengthened its ability to anticipate, detect, and mitigate AI-driven threats, supporting the safe and responsible use of AI in mission-critical environments.

‍