Model Protection: Ensuring Integrated NVIDIA AI Security at Scale
AI models are high-value assets and high-risk targets. This guide introduces the model protection domain, detailing layered defenses like model scanning, runtime security, lifecycle traceability and red teaming to safeguard AI systems from adversarial threats, misuse and operational vulnerabilities. This guide also reveals how NVIDIA NeMo Guardrails transforms governance into real-time enforcement for enterprise AI systems.
WWT's AI Readiness Model for Operational Resilience (ARMOR) framework
World Wide Technology's AI Readiness Model for Operational Resilience (ARMOR) is a practical framework for securing AI and high-performance computing environments. ARMOR is built on real-world expertise and addresses the most urgent security challenges organizations face today. The framework is organized into different domains, each offering actionable guidance and maturity models for organizations at any stage of their AI journey:
- AI Governance, Risk and Compliance
- Secure AI Operations
- Model Protection and AI Application Security
- Software Development Lifecycle (SDLC)
- Infrastructure Security
- Data Protection
- Cyber Resilience
Each domain is authored by subject matter experts and provides practical, vendor-agnostic insights for building resilient, compliant and innovative AI ecosystems.
Model protection and AI application security: What you'll find inside
AI models are high-value assets and high-risk targets. This section introduces the Model Protection domain, detailing layered defenses like model scanning, runtime security, lifecycle traceability and red teaming to safeguard AI systems from adversarial threats, misuse and operational vulnerabilities.
Key principles and strategies
- Security by design: Build zero trust network zones, use infrastructure as code templates with built-in guardrails and apply defense in depth across the AI lifecycle.
- Model security: Scan models for vulnerabilities, backdoors, adversarial susceptibility and bias before deployment. Use model gateways, access controls and encryption to protect models in use and at rest.
- API and runtime security: Monitor and control API access, validate inputs, detect threats and enforce authentication and rate limiting. Use runtime guardrails and firewalls to keep outputs and prompts safe and policy-aligned.
- Lifecycle traceability: Track the origin, integrity and lineage of all AI artifacts. Maintain audit trails, apply watermarking and fingerprinting, and use an AI bill of materials to support transparency and supply chain security.
- Red teaming and adversarial testing: Simulate attacks, test detection and containment, and integrate findings into model retraining and policy updates.
- Organizational accountability: Define governance policies, assign roles and establish risk management ownership for AI systems.
Maturity model
ARMOR provides a maturity model for model protection and AI application security, helping organizations benchmark their current practices and chart a path from ad hoc, fragmented controls to integrated, automated and regulatory-aligned security. The model covers everything from basic policy development and tool rationalization to centralized governance, automated AI security operations and continuous improvement.
Relevant frameworks and standards
ARMOR aligns with leading frameworks such as the NIST AI Risk Management Framework, ISO/IEC 42001, GDPR, CIS Critical Security Controls and OWASP. These frameworks help organizations benchmark, mature and align their model protection and application security initiatives with industry best practices and regulatory requirements.
Who should read this and why
This section is for AI architects, security engineers, data scientists and anyone responsible for protecting AI models and applications from evolving threats. If your organization is deploying AI at scale, managing sensitive models or facing new risks from adversarial attacks, the Model Protection and AI Application Security domain provides practical insights and actionable steps to strengthen your security posture.
Why keep reading
- Learn how to implement layered defenses for AI models and applications
- Find best practices for model scanning, runtime security, lifecycle traceability and red teaming
- Benchmark your organization's maturity and identify steps for improvement
- Build confidence that your model protection strategy is robust, scalable and aligned with global standards
Unlock the full report for in-depth guidance, implementation approaches and expert insights to help you protect your AI models and applications from today's most advanced threats.
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