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AI SECURITY SERVICES

Security That Keeps Pace With Your Innovation

AI is reshaping how organizations build, deploy, and scale applications—but it also creates new pathways for attackers. UltraViolet Cyber helps you secure AI-enabled systems with proactive testing, governance, and model‑aware risk assessments that adapt to real-world threats.  

AI Threat Modeling

AI systems introduce unique risks—from hidden data flows to complex model behaviors—that traditional application threat modeling simply doesn’t capture. Our AI Threat Modeling service provides a structured, model‑aware evaluation of how your AI application could be misused, manipulated, or compromised, and what controls are needed to secure it. 

What's Included

Design & configuration reviews tailored to your model and platform

We analyze architectures, integrations, model endpoints, training pipelines, and platform configurations to identify risks specific to your AI environment—not just your application surface.

Context-driven threat modeling that highlights key risks

Our threat models reflect how your AI solution actually operates—its data pathways, decision logic, access points, and dependencies—ensuring risks are prioritized based on your real deployment context.

Identification of data and model security vulnerabilities

We pinpoint areas where adversaries could exploit your system, including model manipulation, data leakage, prompt injection, alignment failures, privilege escalation, or insecure training artifacts.

AI Threat Modeling

AI systems introduce unique risks—from hidden data flows to complex model behaviors—that traditional application threat modeling simply doesn’t capture. Our AI Threat Modeling service provides a structured, model‑aware evaluation of how your AI application could be misused, manipulated, or compromised, and what controls are needed to secure it.

 

What's Included
Design & configuration reviews tailored to your model and platform
We analyze architectures, integrations, model endpoints, training pipelines, and platform configurations to identify risks specific to your AI environment—not just your application surface. 
Context-driven threat modeling that highlights key risks

Our threat models reflect how your AI solution actually operates—its data pathways, decision logic, access points, and dependencies—ensuring risks are prioritized based on your real deployment context.

Identification of data and model security vulnerabilities

We pinpoint areas where adversaries could exploit your system, including model manipulation, data leakage, prompt injection, alignment failures, privilege escalation, or insecure training artifacts.

Outcome

You receive a clear, actionable understanding of your AI system’s risk profile—along with prioritized recommendations to strengthen controls, reduce exposure, and safely scale your AI capabilities.

 

AI RED TEAMING

Our AI Red Teaming service provides a realistic, adversary-driven evaluation of your AI and ML systems to uncover how they can be attacked, misused, or manipulated in real-world conditions. This assessment goes beyond traditional testing to examine the unique threat landscape introduced by modern AI deployment models.

 

 

Attacker simulation against AI/ML deployments and underlying platforms
We emulate real threat actor behaviors to understand how your AI systems respond under offensive pressure, from prompt manipulation to model exploitation.
Baseline security review of platform controls
We assess the security posture of the infrastructure supporting your AI workloads, ensuring controls are properly configured, monitored, and resilient against misuse.
In‑depth assessment of AI/ML models
Using specialized tools and methodologies, we evaluate models used internally or exposed to end users—identifying vulnerabilities, misalignment risks, and pathways for unauthorized access or data leakage.

Outcome

You gain a clear understanding of how real attackers could compromise your AI systems—across models, pipelines, and platform layers—along with actionable guidance to eliminate vulnerabilities, strengthen controls, and prevent high‑impact exploitation. This enables your organization to deploy and scale AI confidently, with security embedded from the model layer to the application surface.

 

AI /ML Governance Strategy

Successful AI adoption requires more than secure models—it demands a governance framework that ensures your AI systems are safe, responsible, compliant, and aligned with organizational objectives. Our AI / ML Governance Strategy service helps you establish the standards, processes, and operational maturity needed to deploy AI confidently and at scale.

 

What's Included
Program strategy, enablement, and governance for AI/ML deployments
We work with your teams to define governance structures, assign ownership, and establish the policies and controls necessary for secure and responsible AI use across the organization.
Gap identification and standards development

We evaluate your current AI practices to uncover gaps across risk management, compliance, data handling, and model lifecycle processes—and create new standards to close those gaps.

AI/ML Maturity Action Plans (MAP)

Our MAP framework outlines clear, actionable steps to advance your AI governance maturity, from foundational readiness to advanced, enterprise‑wide deployment.

Secure LLM development practices
We guide your teams on secure model design, fine‑tuning, data governance, and guardrail implementation to ensure your LLMs behave safely and predictably.
Custom instructor‑led training (ILTs)
We deliver tailored training programs for engineering, data science, governance, and security teams—building internal capability and ensuring long‑term sustainability.

Outcome

You gain a scalable, repeatable, and compliant governance model that supports secure AI innovation—empowering your teams to build responsibly while reducing organizational risk.