Secure AI Systems Across Their Lifecycle
Artificial intelligence is transforming how organizations operate, but it also introduces new security challenges that extend beyond traditional cybersecurity. AI models, large language models (LLMs), AI agents, and AI-powered applications require dedicated security controls to protect against unauthorized access, data exposure, model abuse, and emerging AI-specific threats.
Our AI Security service helps organizations assess, design, and implement security controls that protect AI systems throughout their lifecycle—from development and deployment to ongoing operations.


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 $5,500
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)