AI Security – AI Red Teaming

Secure AI Systems Across Their Lifecycle

Artificial intelligence is transforming how organizations operate, innovate, and serve their customers, but its growing adoption also introduces new security challenges that traditional cybersecurity controls may not fully address. AI models, large language models (LLMs), AI agents, and AI-powered applications can create new risks involving unauthorized access, sensitive data exposure, model manipulation, prompt injection, insecure integrations, and emerging AI-specific threats.

Our AI Security service helps organizations identify and address these risks before they become costly security incidents. We assess AI systems, their underlying infrastructure, data flows, APIs, integrations, and access controls to uncover weaknesses that could be exploited by attackers or misused by users.

We help design and implement practical security controls throughout the AI lifecycle—from development and testing to deployment and ongoing operations. Our approach combines AI-focused security assessments, threat analysis, vulnerability testing, access protection, data security, and continuous risk management to help organizations deploy AI with greater confidence.

Whether you are developing a new AI solution or securing an existing system, we help strengthen its security posture, protect sensitive information, reduce exposure to emerging threats, and build a more resilient AI environment.

What We offer

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AI Security Architecture Reviews

We evaluate the design and architecture of your AI environment to identify security weaknesses before they become operational risks. Our review assesses how AI systems, models, infrastructure, and supporting services interact, providing recommendations to improve security, scalability, and resilience.

AI Application Security Assessments

We assess AI-powered applications for security vulnerabilities that could impact confidentiality, integrity, or availability. This includes reviewing application components, authentication, authorization, data handling, integrations, and AI-specific attack surfaces to identify security risks and recommend remediation.

Large Language Model (LLM) Security

We assess the security of LLM-based applications to identify risks such as prompt injection, sensitive data exposure, insecure system prompts, excessive permissions, model misuse, and insecure integrations. Our assessments help organizations strengthen the security of AI-powered assistants, chatbots, and enterprise LLM deployments.

Agentic AI Security

We evaluate autonomous AI agents and multi-agent systems to identify security risks associated with decision-making, memory, tool usage, permissions, and interactions with external systems. Our assessments help ensure AI agents operate securely, reliably, and within defined security boundaries.

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AI API Security

We assess APIs that enable AI functionality to identify authentication weaknesses, authorization issues, insecure configurations, data exposure risks, and vulnerabilities that could allow unauthorized access or abuse of AI services.

Identity and Access Management for AI Systems

We review how users, administrators, applications, and AI agents authenticate and access AI resources. Our assessment focuses on role-based access controls, privileged access management, authentication mechanisms, and the principle of least privilege to reduce the risk of unauthorized access.

AI Data Protection and Privacy Controls

We assess how sensitive information is collected, processed, stored, and transmitted within AI environments. Our review evaluates data handling practices, privacy controls, retention policies, encryption, and access controls to help protect confidential business and customer information.

Secure AI Deployment Reviews

We review AI deployments before or after implementation to ensure security controls are appropriately configured. This includes evaluating deployment architecture, cloud configurations, infrastructure security, environment separation, monitoring capabilities, and operational security practices.

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AI Governance and Security Framework Development

We help organizations establish governance processes that support the secure and responsible use of AI. This includes developing AI security policies, governance frameworks, risk management processes, security standards, and implementation guidance aligned with organizational objectives.

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AI Security Consulting and Remediation Guidance

Following our assessment, we provide practical recommendations to help organizations strengthen their AI security posture. We work with internal teams to prioritize findings, develop remediation plans, improve security controls, and support the implementation of recommended security measures.

Why AI Security Is Important

Organizations increasingly rely on AI to automate business processes and support critical decision-making. Without appropriate security controls, AI systems may be exposed to risks including:

Unauthorized access
Sensitive data disclosure
Prompt injection attacks
AI model manipulation
Excessive permissions
Insecure integrations
Regulatory and compliance gaps
Operational disruption

A structured AI security program helps reduce these risks while improving resilience, governance, and trust in AI-enabled services.

Industries We Support

Our AI Security services are suitable for organizations across multiple sectors, including:

Financial Services
Healthcare
Technology
Manufacturing
Retail and E-commerce
Government
Telecommunications
Energy
Professional Services

AI Security Pricing

Starting at $4,000

Our AI Security engagements are tailored to the size, complexity, and maturity of your AI environment. Pricing is based on the scope of the assessment and the security services required.

What’s Included

AI security architecture review
AI application security assessment
Large Language Model (LLM) security review
AI agent security assessment
API and integration security review
Identity and access management assessment
Data protection and privacy review
AI governance and security controls review
Risk identification and prioritization
Security recommendations
Executive summary
Detailed technical report
Remediation roadmap
Final consultation and review

Pricing Factors

The final project cost depends on:

Number of AI applications and models
AI agents in scope
APIs and third-party integrations
Cloud or on-premise deployment
Complexity of the AI environment
Regulatory and compliance requirements
Assessment depth
Number of environments (development, staging, production)