Threats are hiding in your data.
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Mission Challenge
Modern AI systems expose a massive attack surface.
Adversaries can trick AI models into misidentifying targets and assets in high-risk mission operations — and these attacks are inexpensive, easy to implement, and invisible to standard security systems. Not the network. Not the system. The model, the data, and the mission outcomes are the target — below the visibility threshold of every cyber tool in your current stack.
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Nights Watch Solution
Nights Watch secures your entire AI decision cycle before adversaries exploit it.
Nights Watch is a robust security layer designed to ensure AI validity from development through active deployment. The platform delivers total visibility into data and model vulnerabilities, putting proactive posture management directly in the hands of the operator. Mission intelligence remains a strategic asset—not a vulnerability—enabling 100% operational confidence.
Find weaknesses and harden models before deployment
Nights Watch subjects your AI systems and workflows to rigorous stress testing. Know exactly how a model fails, how badly, and where—and how to fix it.
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Operational Use Cases
Deny adversary exploits across the digital kill chain.
Close the Vulnerability Gap. Adversaries don’t just attack networks—they manipulate the critical data feeding your tactical AI. Nights Watch secures the high-risk friction point between raw sensor ingestion and real-time mission execution.
Field-tested Evaluations
detection accuracy against adversarial attacks
model performance recovery during live incidents
Real-time detection and response at the edge
Zero-code deployment across environments
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The Nights Watch Difference
Purpose-engineered for the visual data domain
Most AI security tools focus on language models and networks. Nights Watch delivers a specialized, hardened technical integrity layer built explicitly for high-consequence visual and sensor-driven defense AI where generic solutions fail to operate.
Adversarial AI Threat Brief
Alarmingly easy. Unlike traditional cyberattacks, compromising an AI vision pipeline requires zero privileged user access, zero system credentials, and no network exploits. Instead, adversaries exploit the inherent mathematical vulnerabilities of neural networks—meaning they only need to manipulate the data or physical environment your sensors see to completely subvert your mission outcomes.
Adversaries exploit the scale and openness of modern AI pipelines using two primary, highly accessible execution methods:
In the Field (Inference-Stage): An attacker applies a highly calculated physical pattern, sticker, or digital overlay—covering as little as 10% of the target—to trick a Computer Vision model. Because neural networks rely heavily on localized pixel textures, these adversarial patches can completely force a Target Suppression state (making an asset invisible) or Misclassification (disrupting the digital kill chain).
Upstream (Training-Stage): By injecting a tiny fraction of subtly altered or mislabeled samples into open-source datasets, public repositories, or unverified data collection loops, an adversary bakes a permanent, dormant backdoor directly into the model’s weight architecture. The model will pass standard validation flawlessly, but will instantly fail when triggered by a specific real-world activation pattern in the field.
An adversary applies a highly engineered physical pattern or digital overlay to a target (e.g., a vehicle or installation). This pattern is designed to exploit mathematical blind spots within a deployed Computer Vision model. When the EO/IR targeting model processes the asset, the patch effectively erases or alters the target in the output with high confidence, breaking the digital kill chain.
Target Suppression: Renders high-value tactical assets completely invisible to detection and AI vision models, transforming them into background terrain or empty space.
Misclassification: Intentionally tricks a model into misidentifying a specific threat as something entirely benign—such as misclassifying an active threat vehicle as a civilian asset.
Data poisoning occurs upstream during the pre-training or fine-tuning phases. An adversary injects subtly altered, malicious samples into public repositories, open-source datasets, or unverified collection pipelines. This embeds a dormant backdoor into the model’s weight architecture. The model will perform flawlessly under standard validation, but it will immediately fail or execute a pre-programmed malicious action when triggered by a specific real-world activation pattern (like a hat).
Defend the data. Secure the AI. Assure the outcome.
Without resilience, AI is a liability in contested/edge environments. AI whose integrity holds is a shared, scalable decision asset. As you move from AI experimentation to operational deployment, don’t sacrifice security or trust. Schedule a threat briefing with our solutions team. We’ll show you exactly what’s at risk — and how Nights Watch can protect it.
Nights Watch is available on CDAO Tradewinds.
Find us on the regular and SBIR/STTR Aisles. Government buyers need to register with the Marketplace to access our solution.
