Security teams tell us that AI red teaming today is too slow, too noisy, and too expensive.
All approaches on the market currently rely on volume, whereby thousands of prompts are fired at an AI model or agent in the hope that something breaks. Teams are left sifting through a flood of outputs, many unrelated to their use case, creating false positives and alert fatigue while burning time, compute, and budget before uncovering anything relevant or meaningful.
Real attackers don’t go after targets blindly. By starting with Mindgard’s Reconnaissance, AI red teaming becomes orders of magnitude faster and cheaper. Instead of following a ‘spray and pray’ approach that forces users to launch a huge number of attack prompts in the hopes of getting a hit, Mindgard’s approach first conducts tactical reconnaissance to reveal the AI’s hidden attack surface and risky behavior to then subsequently drive automated AI red teaming with surgical effectiveness.
To demonstrate why reconnaissance is a critical component within AI red teaming, we draw from a real-world parallel. Pulling off a successful bank heist requires lots of meticulous planning and intel, including scoping out the perimeter, learning employee rotations, acquiring the building blueprints, and identifying staff with access to the vault. Not taking these steps would undoubtedly lead to operation failure, committing excessive time and resources with minimal return, as well as an assortment of false flags.
Existing AI red teaming approaches suffer from the identical problem whereby they are focused on launching a massive barrage of different prompt injection and jailbreak benchmarks against an AI model or agent in the hopes of receiving an answer deemed as harmful or unsafe.
Some solutions even require their customer to supply information about their own systems for AI red teaming to be effective, which limits their ability to expose critical blindspots and unknown risks manifesting within their AI infrastructure that the customer hasn't foreseen.
Risk emerges from the system around the model; the guardrails, system prompt, tool-calling layer, encoding behaviors, rendering logic, and more. These layers define what attackers can discover and leverage. Without systematically mapping this architecture first, existing AI red teams test blindly instead of precisely, wasting cycles attacking vectors that do not matter while missing the ones that do.
Today we are introducing Mindgard Reconnaissance, the first capability in any AI security platform designed to reveal early intelligence about an AI system before deeper adversarial testing begins.
Mindgard Reconnaissance automates the intelligence-gathering phase of AI red teaming. It turns fragmented experimentation into structured, repeatable insight about how an AI system actually behaves. Instead of relying on slow and expensive benchmark-driven testing, teams can move directly into targeted red teaming that focuses on the real attack surface.
The reconnaissance tactics, techniques and procedures, created by our world-class AI security research team, which have uncovered vulnerabilities in leading AI systems including OpenAI ChatGPT Sora, Google Antigravity, Google Gemini Search AI, Zed IDE, Cursor and others, are now fully operationalized into the Mindgard platform and available to our customers.
Before attempting bypasses, exploitation, or exfiltration, Mindgard Recon systematically gathers intelligence about the deployed AI system. The goal is not to generate noise, but to enumerate the environment with precision. This approach of Discover, Plan, and Attack is the cornerstone of any red teamer’s methodology, and for the first time is now available versus AI systems and infrastructure.
Recon encompasses various novel techniques. It identifies the existence of guardrails and fingerprints their capability. It extracts and analyzes underlying system prompts to surface hidden instructions, logic gaps, or sensitive configuration artifacts influencing behavior. It enumerates exposed tools and their parameters, revealing how the model or agent interacts with backend services, internal APIs, MCP servers, and data sources. It tests encoding capabilities to determine whether they can be leveraged to mask malicious payloads. It analyzes output formatting and rendering behavior to understand whether structured formats are supported.
This systematic reconnaissance changes how payloads are crafted. Instead of launching broad, high-volume prompt campaigns and hoping for a signal, testing becomes guided by confirmed system behavior. Payloads are selected based on known guardrail presence, discovered tool schemas, AI behavioral weaknesses, exposed formatting capabilities, and extracted system logic. Exploitation becomes targeted rather than probabilistic.
Same as meticulously planning a bank heist, Mindgard Reconnaissance results in not just less expensive testing, but materially more efficient testing. Fewer prompts are required because they are informed. High-risk findings present faster because they are aligned with real exposure rather than speculative scenarios.
Reconnaissance changes the ROI of red teaming. Security teams no longer spend half of an engagement figuring out what they are testing. Setup time drops, time to first high risk finding is slashed.
Findings are evidence-backed:
These artifacts can be exported, shared with blue teams, integrated into CI pipelines, or attached to executive reporting. Instead of “we tried some things and nothing broke,” teams present defensible, reproducible intelligence.
Attack surface measured: CISOs are empowered to establish and maintain an executive view of their organization’s attack surface when it comes to their AI models, agents, and infrastructure. Clear and actionable risk profile of AI assets under management.
Accuracy increases: Payloads are crafted based on a confirmed attack surface rather than blind experimentation, findings are more precise. Noise decreases. False positives are reduced. Analyst effort focuses on real exposure.
Lower costs: Red teams complete engagements faster. Fewer prompts are required. Less manual iteration is needed. Retesting after remediation becomes replayable and automated.
Many tools in the market focus on model evaluation or compliance-style validation. They measure whether responses adhere to policy or align with benchmarks. They rarely attempt structured system or infrastructure-level reconnaissance.
Mindgard Reconnaissance focuses on identifying the attack surface so precisely that exploitation becomes efficient rather than probabilistic.
It does not treat the model as an isolated artifact. It treats the AI system as a layered environment where instructions, tools, formatting behaviors, and guardrails interact in complex ways. This system-level perspective is where real enterprise risk lives.
AI security buyers should expect more than volume-based testing and generic prompt benchmarks. The right solution should demonstrate precision, visibility into the true attack surface, and measurable efficiency in how risks are discovered. CISOs evaluating AI security platforms should ask vendors a few simple questions to determine whether they are delivering meaningful insight or just generating noise.
AI is advancing quickly. Agents are being connected to tools and backend systems. The attack surface area grows with every integration. If attackers are performing reconnaissance activities before exploiting your AI, you should level the playing field. Reconnaissance helps you see beyond the prompt and understand your AI environment the way an adversary would. This precision eliminates waste. Eliminating waste creates ROI.
Orders of magnitude more effective.
A fraction of the prompts.
A fraction of the cost.
That is where real AI security begins.
For more information visit: https://docs.mindgard.ai/user-guide/reconnaissance/overview
