SECURING YOUR AI JOURNEY

Enable AI-Led Growth
Without Expanding Enterprise Risk

AI doesn’t just increase productivity. It concentrates privilege, expands attack surface, and automates risk. If your AI systems are secured the same way as legacy software,  you are blind to the most dangerous behaviors.

UltraViolet Cyber provides security services across the AI lifecycle, combining strategy, threat modeling, adversarial testing, monitoring, and training to support secure AI adoption.
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Securing Your AI Journey

Enable AI-Led Growth, Without Expanding Enterprise Risk.

Every major enterprise is integrating AI into core operations. But AI doesn’t just increase productivity. It concentrates privilege, expands attack surface, and automates risk. If your AI systems are secured the same way as legacy software,  you are blind to the most dangerous behaviors.

UltraViolet Cyber provides security services across the AI lifecycle, combining strategy, threat modeling, adversarial testing, monitoring, and training to support secure AI adoption.

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Securing Your AI Transformation

 

AI systems are dynamic, probabilistic, and continuously evolving.

That changes how risk must be managed.

The Enterprise AI Risk Shift

AI introduces a distinct operational risk profile embedded directly into enterprise systems, data pipelines, and decision flows. As models, agents, and automation scale across the organization, security must operate continuously and in alignment with how AI behaves in production environments.

 

Where Risk Is Emerging

AI Agents & Autonomous Workflows

  • Privileged API chaining

  • Goal hijacking and unintended execution

  • Prompt injection attacks

  • Lateral movement through AI integrations

  • Data leakage via workflow automation

AI Infrastructure & Model Expansion

  • Model theft and IP exfiltration

  • GPU and compute targeting

  • Supply-chain compromise of third-party models

  • Data poisoning and model manipulation

AI-Enabled Products & Customer Features

  • Hallucinated outputs influencing business decisions

  • Exposure of regulated or sensitive customer data

  • Abuse of generative interfaces

  • Regulatory scrutiny over automated outcomes

Enterprise AI Initiatives and Expected Security Outcomes

AI adoption typically shows up in a consistent set of enterprise initiatives. Each one benefits from clear security outcomes that keep innovation moving.

1. Enterprise AI Agents for Automation and Decision Support
Common exposure

Privileged access abuse, data leakage, unintended actions.

How UV helps

AI penetration testing and adversarial evaluation to identify security gaps across models, APIs, and workflows.

2. AI-Enabled Cloud Migration and Modernization
Common exposure

Expanded attack surface, misconfigurations, inconsistent controls across environments.

How UV helps

Validate AI pipelines and cloud integrations through continuous testing and targeted assessments.

3. AI Infrastructure Expansion (AI Compute, Data Centers, and Platforms)
Common exposure

High-value infrastructure targeted for IP theft, supply-chain compromise, and disruption of critical compute resources.

How UV helps

Continuous monitoring of AI platforms to detect and investigate anomalies, plus risk-informed hardening guidance.

4. Embedding AI into Core Products
Common exposure

Model manipulation, data poisoning, and regulatory exposure as AI impacts business outcomes.

How UV helps

Adversarial model evaluation to understand behavior under malicious inputs and corrupted data, paired with governance and controls.

5. AI-Driven Productivity and Customer-Facing Features
Common exposure

Customer data exposure, prompt injection abuse, and reputational risk from AI-generated errors or misuse.

How UV helps

Test AI-enabled applications and monitor production signals to detect misuse patterns and runtime anomalies.

Where Are You in Your AI Journey?

Most organizations are pursuing one or more of these AI projects today. Which initiative is your team building right now?

Enterprise AI Agents for Automation and Decision Support AI-Enabled Cloud Migration and Modernization Expansion (AI Compute, Data Centers, and Platforms) Embedding AI into Core Products AI-Driven Productivity and Intelligent Workflows (Chatbots, Copilots, Internal Assistants)
system-vulnerability-1

Common Exposure

Privileged access abuse, data leakage, unintended actions.
security-update (1)

How UltraViolet Helps

AI penetration testing and adversarial evaluation to identify security gaps across models, APIs, and workflows.

Penetration Testing Threat Modeling Training

Security Services Across the AI Lifecycle

An end-to-end approach to AI security combining strategy, threat modeling, adversarial testing, monitoring, and training to support secure AI adoption.

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AI Program Strategy & Governance

Define how AI is used, governed, and secured across the organization.

  • AI maturity assessments
  • Build vs. buy security advisory
  • AI risk and governance frameworks
  • AI security policy development
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AI Threat Modeling

Identify AI-specific attack paths before systems go live.

  • LLM application threat modeling
  • Agent and workflow risk analysis
  • Prompt injection and data exposure scenarios
  • Model lifecycle and pipeline risk mapping
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AI Penetration Testing

Simulate how adversaries will target AI systems in practice.

  • LLM application testing
  • Prompt injection and jailbreak scenarios
  • Data extraction and model manipulation
  • API and AI integration security testing
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Managed SOC

Continuously monitor AI-enabled systems in production.

  • Detection of AI misuse and anomalies
  • Data leakage and privileged escalation monitoring
  • Runtime model behavior monitoring
  • Integration with existing SOC environments
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Instructor-Led Training

Equip teams to build and operate AI systems securely.

  • Principles of AI/ML Security
  • Threat Modeling for AI/ML Systems
  • Security Champions programs for AI development teams
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AI Security In Practice

Technology Company Secures Customer-Facing AI Chatbot

Technology

A technology company preparing to launch an LLM-powered chatbot for customer support engaged UltraViolet Cyber to validate the security of the application before broad deployment.

The organization needed assurance that sensitive customer data and backend systems could not be exposed through prompt manipulation or model misuse.

What We Tested

  • LLM-powered customer support chatbot
  • Prompt injection and jailbreak scenarios
  • Sensitive data exposure and output leakage risks
  • Guardrail enforcement and role-based access controls

Outcomes

  • Privacy and regulatory exposure reduced before launch
  • Chatbot validated against prompt injection and system prompt leakage
  • Security improvements implemented to support safer scaling of the AI application
“
We needed confidence that our chatbot could withstand real adversarial interactions before making it widely available to customers.

Nonprofit Organization Reduces AI Chatbot Risk

Healthcare & Life Sciences

A nonprofit organization engaged UltraViolet Cyber to assess the security of an internally developed AI-powered customer service chatbot used to assist members.

The team had implemented security controls and wanted to validate that the chatbot could withstand real-world adversarial scenarios.

What We Tested

  • AI customer support chatbot
  • Prompt manipulation and policy bypass scenarios
  • Guardrail enforcement and safety controls
  • Custom adversarial payloads simulating reputational risk scenarios

Outcomes

  • AI misuse and unsafe output risks identified and mitigated
  • Existing guardrails validated under real adversarial testing
  • Security controls strengthened to support safe deployment and ongoing operation
“
UltraViolet helped us understand how our chatbot could behave under real-world attack conditions and where additional safeguards were needed.

The UltraViolet AI Security Framework

We secure your AI journey across eight foundational domains.

  • 01 Strategy & Governance Define how AI is used, owned, and controlled.
  • 02 Risk Management Identify and mitigate risks unique to AI systems.
  • 03 Data Security & Privacy Protect training and inference data.
  • 04 Model Security & Integrity Prevent tampering, poisoning, and theft.
  • 05 Application Security Extend secure SDLC to AI-enabled components.
  • 06 Monitoring & Detection Detect AI misuse, anomalies, and drift.
  • 07 Compliance & Regulatory Alignment Prepare for emerging AI regulatory requirements.
  • 08 Awareness & Training Build an organization that uses AI safely and effectively.

Unlike siloed providers, we connect offensive validation with continuous defense — creating a closed feedback loop between testing and monitoring.

Enterprise AI Initiatives and Expected Security Outcomes

AI adoption typically shows up in a consistent set of enterprise initiatives. Each one benefits from clear security outcomes that keep innovation moving.

1. Enterprise AI Agents for Automation and Decision Support
Common exposure

Privileged access abuse, data leakage, unintended actions.

How UV helps

AI penetration testing and adversarial evaluation to identify security gaps across models, APIs, and workflows.

2. AI-Enabled Cloud Migration and Modernization
Common exposure

Expanded attack surface, misconfigurations, inconsistent controls across environments.

How UV helps

Validate AI pipelines and cloud integrations through continuous testing and targeted assessments.

3. AI Infrastructure Expansion (AI Compute, Data Centers, and Platforms)
Common exposure

High-value infrastructure targeted for IP theft, supply-chain compromise, and disruption of critical compute resources.

How UV helps

Continuous monitoring of AI platforms to detect and investigate anomalies, plus risk-informed hardening guidance.

4. Embedding AI into Core Products
Common exposure

Model manipulation, data poisoning, and regulatory exposure as AI impacts business outcomes.

How UV helps

Adversarial model evaluation to understand behavior under malicious inputs and corrupted data, paired with governance and controls.

5. AI-Driven Productivity and Customer-Facing Features
Common exposure

Customer data exposure, prompt injection abuse, and reputational risk from AI-generated errors or misuse.

How UV helps

Test AI-enabled applications and monitor production signals to detect misuse patterns and runtime anomalies.

Built by Operators.
Designed for Production.

AI security is defined in production environments where models, agents, and automation interact with live systems and data. UltraViolet brings operational rigor, adversarial depth, and continuous monitoring to ensure those systems perform securely at scale.

Why UltraViolet Cyber?

  • 01 Built by Operators Founded by former U.S. intelligence community operators, UltraViolet brings offensive DNA to defensive strategy. We don’t just know how to detect threats, we know how attackers think.
  • 02 Flexible Engagement Models Whether co-managed, fully outsourced, or embedded, we adapt to your team, your tech stack, and your mission.
  • 03 Federal-Grade Operational Rigor Trusted by DHS and Fortune 500 enterprises alike, UltraViolet delivers operational rigor with agile execution.
  • 04 Unified Red, Blue, and Purple Operations
     UltraViolet operates offensive, defensive, and purple team capabilities as a unified model, creating tighter coordination across testing, detection, and response.
  • 05 Compliance-Driven Assurance
     UltraViolet helps organizations meet audit, regulatory, and customer assurance requirements while strengthening security in practice across testing, monitoring, and governance.

 

Built by Operators.
Designed for Production.

AI security is defined in production environments where models, agents, and automation interact with live systems and data. UltraViolet brings operational rigor, adversarial depth, and continuous monitoring to ensure those systems perform securely at scale.

Red Team Adversarial Simulation
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Blue Team 24x7 Monitoring
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Purple Team Continuous Validation
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Federal-Grade Operational Rigor
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Vendor-Agnostic SOC Integration
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Understand your AI exposure before it becomes a headline.

AI should accelerate your business, not accelerate unmanaged risk.

Start with an AI Security Program Assessment to gain a confidential, data-driven view of your current AI security posture, benchmark it against peer organizations, and receive a clear, prioritized roadmap for strengthening governance, engineering controls, and runtime protection.

Security Maturity Assessment

What You Receive

Confidential AI Security Readout
A private assessment of your organization’s AI security posture across strategy, governance, engineering, assurance, and runtime monitoring domains.

Benchmarking Against Peers
Comparative insights showing how your industry approaches AI risk, highlighting where you lead and where additional focus may be required.

Practice-Level Maturity Scoring
Clear visibility into progression signals across key practices such as data governance, model lifecycle management, AI-augmented SDLC, runtime monitoring, and incident response.

Activity-Level Gap Analysis
Binary visibility into which specific security activities are performed today and which are not — translating AI strategy into observable controls.

Prioritized Improvement Roadmap
A structured progression model outlining practical next steps to move from Emerging to Established maturity levels.
GUIDE

6 Strategic MSSP Capabilities for Securing Enterprise AI

Not every MSSP is ready for AI.

Download this guide to learn the six capabilities your provider needs to secure AI systems and how to assess your current coverage.

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UltraViolet Cyber - ebook - Six Strategic MSSP Capabilities for Securing Enterprise AI