AI Security Needs More Than Guardrails.
Most solutions stop at blocking prompts. We secure the data, models, and pipelines behind real AI initiatives, giving security leaders the enterprise framework they need to move fast without breaking trust.
AI Adoption Is Outrunning Security
Organizations are deploying generative AI at an unprecedented pace, but security programs haven't kept up. The result is a rapidly expanding attack surface that traditional cybersecurity frameworks weren't designed to address.
Sensitive Data Exposure in GenAI Tools
Every time an employee pastes proprietary code, customer data, or financial projections into a large language model, that information enters a system your security team doesn't control. Training data ingestion, prompt logging, and third-party API forwarding create multiple vectors for data leakage that traditional DLP solutions can't detect.
Prompt Injection and Jailbreak Attacks
Prompt injection has emerged as the most accessible attack vector against AI systems, requiring no specialized tools and exploiting the fundamental architecture of language models. Attackers craft inputs that override system instructions, extract training data, or manipulate model behavior.
Model Theft and Adversarial Machine Learning
Fine-tuned models represent millions of dollars in intellectual property. Model extraction attacks use carefully crafted queries to reconstruct model behavior without accessing the underlying weights. Adversarial examples can undermine AI-driven decision making in production.
Regulatory Pressure Is Accelerating
The EU AI Act introduces mandatory risk classification and compliance requirements. NIST's AI Risk Management Framework is rapidly becoming the de facto standard. ISO 42001 provides the first international certification standard for AI management systems.
8 Domains. One Integrated Program.
Each domain maps to a specific requirement in the NIST AI RMF, EU AI Act, or OWASP LLM Top 10. Together, they form a comprehensive program that turns regulatory complexity into a coherent security practice.
AI Governance and Risk Management
Establish AI risk frameworks, ownership structures, and board-level reporting aligned to NIST AI RMF Govern and EU AI Act Article 9. Define risk appetite, assign accountability, and integrate AI risk into enterprise risk programs.
Data Protection and Governance
Classify and protect training data, inference inputs, and model outputs with AI-aware DLP policies. Enforce data quality, provenance tracking, and consent management per EU AI Act Article 10 data governance requirements.
Prompt Security and Output Integrity
Defend against prompt injection, jailbreaks, system prompt leakage, and data exfiltration through multi-layered input validation and output filtering. Addresses OWASP LLM01, LLM05, and LLM07 risks.
Model Integrity and Robustness
Protect model weights, validate inference accuracy, and harden serving infrastructure against adversarial attacks and data poisoning. Aligned to EU AI Act Article 15 accuracy, robustness, and cybersecurity requirements.
AI Supply Chain Security
Vet third-party models, audit AI vendor practices, and secure model procurement pipelines. Covers OWASP LLM03 supply chain vulnerabilities and NIST AI RMF Map function for contextualizing third-party risk.
Monitoring, Detection, and Response
Detect anomalous model behavior, discover shadow AI usage, and maintain incident response playbooks for AI-specific threats. Supports NIST AI RMF Manage function and EU AI Act Article 12 record-keeping obligations.
Transparency and Human Oversight
Ensure AI systems provide clear decision explanations, maintain human-in-the-loop controls, and prevent misinformation. Addresses EU AI Act Articles 13 and 14 and OWASP LLM09 misinformation risks.
Compliance and Regulatory Mapping
Map controls across NIST AI RMF, ISO 42001, and EU AI Act to streamline audits and demonstrate continuous compliance. Maintain technical documentation and evidence per EU AI Act Article 11.
From Visibility to Autonomy
Every organization starts somewhere. Our maturity model gives you a clear path from initial visibility through proactive governance to fully autonomous AI security operations.
Visibility
Your organization recognizes AI security as a concern but lacks formal policies or dedicated ownership. Shadow AI is prevalent, and teams are adopting tools without security review. The first step is mapping what exists.
AI Jumpstart
- Shadow AI discovery and inventory
- AI asset and data flow mapping
- Initial risk assessment against NIST AI RMF
- Executive briefing and roadmap
Proactive
Policies are documented, ownership is assigned, and foundational controls are in place. AI usage is tracked and approved through a formal governance process. Red team exercises validate controls and identify gaps.
Govern and Test
- Policy framework and acceptable use standards
- Adversarial testing and red team exercises
- Data protection and prompt security controls
- EU AI Act and NIST compliance gap analysis
Preventative
Automated controls enforce AI security policies across the enterprise. Continuous monitoring detects anomalous model behavior and data exposure. Compliance reporting runs continuously rather than on demand.
AI Security Posture Management
- Automated policy enforcement and guardrails
- Continuous model behavior monitoring
- AI security posture management deployment
- Automated compliance reporting and evidence collection
Autonomous and Repeatable
AI security operates as a self-optimizing, repeatable program. Threat detection and response are automated, policies adapt dynamically to new model deployments, and the program scales across business units.
Managed AI SecOps
- 24x7 AI threat monitoring and response
- Dynamic policy management for new deployments
- Predictive risk analytics and trend reporting
- Repeatable playbooks across business units
Where Partners Apply This First
These are the three most common entry points for VARs, distributors, and ISVs building AI security practices for their customers.
Shadow AI Governance
Your customers' employees are using dozens of AI tools that IT doesn't know about. Shadow AI governance starts with comprehensive discovery, then builds the policies and controls to bring unsanctioned usage into a managed, secure framework.
- Full visibility into AI tool usage across the enterprise
- Risk-tiered approval workflows for AI applications
- Data loss prevention controls specific to AI interactions
Securing GenAI Applications
As your customers build customer-facing AI features, they need security embedded from the architecture level. This means implementing prompt injection defenses, output filtering, rate limiting, and abuse detection.
- Secure-by-design AI application architecture
- Multi-layered prompt injection defense
- Runtime monitoring and abuse detection
AI Regulatory Compliance
With the EU AI Act enforcement approaching and NIST AI RMF becoming a procurement requirement, your customers need compliance strategies that unify across all three frameworks simultaneously.
- Unified compliance mapping across three frameworks
- Audit-ready documentation and evidence collection
- Continuous compliance monitoring and gap alerting
Ready to Build an AI Security Practice?
Whether you're a VAR adding AI security to your portfolio or an ISV embedding security into your platform, we'll help you move from concept to revenue.
Talk to a Specialist