Cisco Integrated AI Security and Safety Framework Report
Revolutionizing AI Security: A Unified Framework for the Enterprise
Cisco's Integrated AI Security and Safety Framework (AI Security Framework) is a unified, lifecycle-aware taxonomy and operationalization strategy designed to classify, integrate, and operationalize the full range of AI risks, addressing the fragmentation of existing security guidance. It integrates AI security and AI safety across various modalities, agents, pipelines, and the broader ecosystem. The framework is practical for threat identification, red-teaming, and risk prioritization, extensible to emerging AI deployments. It covers 19 objectives, 40 techniques, 112 subtechniques, and procedures, grouped into common manipulation, data-related, and downstream threats. Additionally, it provides a harmful content taxonomy with 25 categories, ensuring comprehensive AI governance. The framework aligns with global policy frameworks and AI regulations, aiming for standardization and integration in the AI security ecosystem.
Executive Impact: Key Metrics in AI Security
Understand the critical statistics driving the need for a comprehensive AI security and safety framework in today's rapidly evolving landscape.
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AI Adoption Growth
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Companies Equipped for AI Threats
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Framework Objectives
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Cisco's Integrated AI Security and Safety Framework provides a unified, lifecycle-aware taxonomy and operationalization strategy for classifying and managing the full spectrum of AI risks. It integrates both AI security and safety across modalities, agents, pipelines, and the broader ecosystem, offering practical guidance for threat identification, red-teaming, and risk prioritization. The framework is designed to be extensible to emerging AI deployments, including multimodal contexts, humanoids, and sensory infrastructures. It addresses gaps in prevailing frameworks by providing a comprehensive structure for understanding AI system failures, adversary exploitation, and defense building across the AI lifecycle.
33%
Only 33% of companies have a formal change management plan for AI adoption.
Enterprise Process Flow
Objectives
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Techniques
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Subtechniques
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Procedures
| Dimension | MITRE ATLAS | NIST AML | OWASP | Industry | Cisco AI Security Framework |
|---|---|---|---|---|---|
| Content safety | No | Partial | No | Partial | Yes |
| AI security | Yes | Partial | Yes | Partial | Yes |
| Lifecycle scope | Partial | Yes | Partial | Partial | Yes |
| Multi-agent/tools | No | Partial | Partial | Partial | Yes |
| Multi-modal | No | Partial | No | Partial | Yes |
| Supply chain | Partial | Partial | Partial | Partial | Yes |
| Unified integration | No | Partial | No | No | Yes |
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Operationalizing the AI Security Framework: Goal Hijacking and Budapest Convention
The Cisco AI Security Framework provides concrete examples of how AI-specific threats relate to established regulatory conventions. For instance, the Goal Hijacking objective (OB-001) directly aligns with concerns highlighted in the Council of Europe's Budapest Convention on Cybercrime. This Convention criminalizes illegal access (Article 2), data interference (Article 4), and system interference (Article 5). The Framework's taxonomy captures the unique nature of AI attacks where adversaries subvert intelligent systems by corrupting their goals rather than their code, an AI-specific manifestation of 'system interference' and 'misuse of devices' (Article 6). Organizations operating in countries ratifying the Budapest Convention can leverage the Framework to understand how to comply by mapping specific AI attack vectors, like goal hijacking, to these legal obligations. This allows for targeted defense strategies that address both technical and regulatory requirements.
Estimate Your Potential AI Security & Safety ROI
Quantify the impact of a comprehensive AI security framework on your organization's operational efficiency and risk mitigation. Adjust the parameters below to see your potential annual savings and reclaimed hours.
Estimated Annual Security & Safety Cost Savings
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Estimated Annual Hours Reclaimed
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Your AI Security & Safety Implementation Roadmap
A phased approach to integrating the Cisco AI Security Framework into your enterprise operations.
Phase 1: Initial Assessment & Gap Analysis
Conduct a baseline assessment of current AI systems, identify existing security postures, and perform a gap analysis against the Cisco AI Security Framework. Prioritize risks based on business impact and regulatory requirements. Define AI security and safety goals.
Phase 2: Framework Integration & Policy Development
Integrate the AI Security Framework into existing risk management and governance structures. Develop or update policies for AI security, data privacy, content moderation, and incident response, ensuring alignment with the framework's objectives and the organization's risk appetite.
Phase 3: Control Implementation & Tooling
Implement technical controls across the AI lifecycle, including output filtering, prompt injection detection, agent sandboxing, RAG integrity, model scanning, and infrastructure security. Deploy monitoring and logging tools tailored to AI-specific threats.
Phase 4: Red-Teaming, Testing & Validation
Conduct targeted red-teaming exercises, adversarial safety testing, and comprehensive security tests against AI systems, covering all techniques and subtechniques identified in the framework. Validate the effectiveness of implemented controls and refine strategies.
Phase 5: Continuous Monitoring & Evolution
Establish continuous monitoring for AI threats, automate incident response playbooks, and ensure ongoing training for AI developers and security teams. Regularly review and update the framework integration to adapt to emerging AI capabilities and threat landscapes.
Secure Your AI Future. Today.
Don't let the evolving AI threat landscape leave your enterprise vulnerable. Partner with Cisco to implement a robust, lifecycle-aware AI Security and Safety Framework. Schedule a personalized consultation to discuss how our integrated approach can protect your AI investments and ensure responsible innovation.
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