AI Security Frameworks Every Organization Should Know
Artificial intelligence is rapidly becoming part of enterprise applications, cybersecurity operations, customer platforms, software development, and business decision-making. While AI can improve productivity and security, it also introduces a new category of risks, including prompt injection, data poisoning, model manipulation, sensitive information disclosure, insecure AI integrations, and supply-chain attacks.
Organizations therefore need structured frameworks to identify, assess, manage, and reduce AI-related security risks.
An AI security framework provides a systematic approach for protecting AI models, applications, data, infrastructure, and users throughout the AI lifecycle. Understanding the major frameworks and standards can help organizations build stronger AI governance and security programs.
What Is an AI Security Framework?
An AI security framework is a collection of principles, controls, practices, and processes designed to manage security risks associated with artificial intelligence.
Traditional cybersecurity frameworks focus primarily on systems, networks, applications, identities, and data. AI security extends this approach to include additional components such as:
- AI models
- Training datasets
- Prompts and instructions
- Model outputs
- AI agents
- Machine-learning pipelines
- Model APIs
- AI plugins and tools
- Retrieval-augmented generation systems
- AI supply chains
A strong AI security program should address risks throughout the entire AI lifecycle—from design and development to deployment, monitoring, and retirement.
Why Organizations Need AI Security Frameworks
AI systems can introduce risks that are different from traditional software vulnerabilities.
For example, an attacker could manipulate a model through carefully crafted inputs, poison training data, extract sensitive information, or exploit an AI application’s connection to external tools.
Without an appropriate framework, organizations may struggle to answer important questions:
- What AI systems are being used?
- What information do they process?
- Who owns each AI system?
- What threats could affect the system?
- What security controls are required?
- How should AI incidents be handled?
- How should AI performance and security be monitored?
Frameworks provide organizations with a structured methodology for answering these questions.
1. NIST AI Risk Management Framework (AI RMF)
The NIST AI Risk Management Framework (AI RMF) is one of the most important frameworks organizations should understand when developing responsible and secure AI programs.
The framework focuses on managing AI risks throughout the system lifecycle and is organized around four core functions:
Govern: Establish policies, responsibilities, accountability, and organizational processes.
Map: Identify and understand AI system context, risks, stakeholders, and potential impacts.
Measure: Analyze, assess, and monitor identified risks.
Manage: Prioritize and address AI risks using appropriate controls and mitigation strategies.
AI RMF is particularly useful for organizations that want to integrate AI risk management into existing enterprise risk-management and cybersecurity programs.
2. NIST Cybersecurity Framework (CSF)
The NIST Cybersecurity Framework is not exclusively an AI framework, but it remains highly relevant to AI security.
Its core cybersecurity functions—Identify, Protect, Detect, Respond, and Recover—can be applied to AI infrastructure and AI-enabled applications.
For example:
Identify: Maintain an inventory of AI systems, models, datasets, and dependencies.
Protect: Implement access controls, encryption, secure configurations, and data protection.
Detect: Monitor AI applications for attacks, abnormal behavior, and unauthorized activity.
Respond: Investigate and contain AI-related security incidents.
Recover: Restore affected systems and improve controls after incidents.
Organizations can combine CSF with AI-specific guidance to create a comprehensive security strategy.
3. OWASP Top 10 for LLM Applications
Organizations using generative AI and large language models should become familiar with the OWASP Top 10 for Large Language Model Applications.
It highlights important security risks associated with LLM-powered applications, including:
- Prompt injection
- Insecure output handling
- Training data poisoning
- Model denial of service
- Supply-chain vulnerabilities
- Sensitive information disclosure
- Insecure plugin or tool design
- Excessive agency
- Overreliance on AI-generated output
- Model-related weaknesses
This resource is particularly valuable for developers and application-security teams building AI-powered applications.
4. MITRE ATLAS
MITRE ATLAS is a knowledge base focused on adversarial threats against artificial intelligence systems.
It provides a structured way to understand how attackers can target machine-learning systems.
Security teams can use ATLAS for:
- Threat modeling
- Adversary emulation
- Security assessments
- Detection engineering
- Red-team exercises
- AI incident response
Examples of AI attack concepts include data poisoning, evasion techniques, model extraction, and other methods designed to manipulate or compromise AI systems.
MITRE ATLAS can complement traditional threat frameworks such as MITRE ATT&CK when organizations are assessing AI-specific attack scenarios.
5. ISO/IEC 42001
ISO/IEC 42001 is an international standard for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS).
Rather than focusing only on technical vulnerabilities, ISO/IEC 42001 addresses organizational governance and management of AI.
It can help organizations establish processes covering areas such as:
- AI governance
- Risk management
- Roles and responsibilities
- Data management
- Transparency
- Monitoring
- Continual improvement
- Responsible AI practices
ISO/IEC 42001 is especially relevant to organizations seeking a formal management-system approach to AI governance.
6. ISO/IEC 27001
ISO/IEC 27001 is an established international standard for information security management systems.
Although it is not specifically designed for AI, it provides a strong foundation for protecting the information and infrastructure used by AI systems.
Organizations can apply information-security controls to:
- AI datasets
- Model repositories
- AI development environments
- APIs
- Cloud infrastructure
- Identity and access management
- Security monitoring
- Vendor relationships
Combining an AI management framework with an information-security management system can provide broader organizational coverage.
7. Google Secure AI Framework (SAIF)
The Secure AI Framework (SAIF) provides security principles for protecting AI systems and AI-powered applications.
It emphasizes concepts such as:
- Extending secure-by-design practices to AI
- Protecting AI infrastructure
- Applying strong identity and access controls
- Monitoring AI systems
- Automating security controls
- Using threat intelligence
- Preparing for AI-specific attacks
SAIF is useful as a practical reference for organizations developing or deploying AI applications, particularly in cloud environments.
8. Cloud Security Alliance AI Guidance
The Cloud Security Alliance (CSA) provides guidance addressing security and privacy considerations for AI systems.
This can be particularly useful for organizations deploying AI workloads in cloud environments.
Important areas include:
- Data security
- Identity management
- Infrastructure security
- AI governance
- Privacy
- Application security
- Third-party risks
Organizations can combine cloud-security practices with AI-specific controls to protect AI workloads from infrastructure-level and application-level threats.
9. CIS Controls
The CIS Critical Security Controls provide prioritized cybersecurity safeguards that can also support AI security.
Organizations can apply relevant controls to AI environments, including:
- Asset inventory
- Software inventory
- Account management
- Access control
- Vulnerability management
- Audit logging
- Malware defenses
- Network monitoring
- Data protection
CIS Controls can serve as a practical operational layer alongside AI-specific frameworks.
10. AI Security and the Secure Software Development Lifecycle
AI security should also be integrated into the organization’s Secure Software Development Lifecycle (SSDLC).
AI applications should undergo security assessment during:
Planning → Design → Development → Testing → Deployment → Monitoring → Retirement
Security teams should perform activities such as:
- Threat modeling
- Secure coding
- Dependency analysis
- Model testing
- Data validation
- Red teaming
- Penetration testing
- Vulnerability management
- Runtime monitoring
This approach helps prevent security from becoming an afterthought.
How These Frameworks Work Together
Organizations do not necessarily need to choose a single framework.
A layered approach can be more effective.
For example:
NIST AI RMF → AI risk management
ISO/IEC 42001 → AI governance and management system
NIST CSF / ISO/IEC 27001 → Enterprise cybersecurity and information security
OWASP LLM Top 10 → LLM application security
MITRE ATLAS → AI adversary tactics and threat modeling
CIS Controls → Practical security safeguards
Cloud security guidance → Protection of AI workloads in cloud environments
Together, these resources can provide governance, risk management, technical security, and operational controls.
Building an AI Security Program
Organizations can begin with a structured process.
Step 1: Create an AI Inventory
Identify every AI system being developed or used across the organization.
Document:
- AI application
- Model
- Vendor
- Data sources
- Business owner
- Technical owner
- Users
- External integrations
- Security classification
Step 2: Classify AI Risks
Evaluate each system based on factors such as:
- Data sensitivity
- Business criticality
- User impact
- Level of automation
- External connectivity
- Regulatory requirements
- Potential attack impact
Step 3: Perform Threat Modeling
Identify how attackers could compromise the AI system.
Consider:
- Prompt injection
- Data poisoning
- Model theft
- Credential compromise
- Supply-chain attacks
- Data leakage
- Malicious tool use
- Unauthorized access
Step 4: Implement Security Controls
Deploy controls appropriate to the identified risks.
These may include:
- Strong authentication
- Least-privilege access
- Encryption
- Network segmentation
- Data-loss prevention
- Secure APIs
- Logging
- Monitoring
- Input validation
- Output filtering
Step 5: Continuously Monitor AI Systems
AI security cannot stop after deployment.
Organizations should continuously monitor:
- Model behavior
- User activity
- API requests
- Security alerts
- Data access
- Model performance
- New vulnerabilities
- Emerging attack techniques
AI Frameworks and Compliance
AI governance is increasingly becoming connected to regulatory and contractual requirements.
Organizations should therefore evaluate how their AI security framework aligns with applicable:
- Privacy requirements
- Data-protection regulations
- Industry standards
- Customer requirements
- Internal policies
- Contractual obligations
Frameworks should not be treated as compliance checklists alone. Their primary purpose should be to reduce real-world risk and improve organizational resilience.
The Future of AI Security Frameworks
AI technology is evolving rapidly, and security frameworks will continue to develop alongside it.
Emerging areas such as autonomous AI agents, multimodal models, AI-generated code, retrieval-augmented generation, and AI-to-AI communication will introduce additional security considerations.
Organizations will increasingly need frameworks that address not only AI models but also the broader AI ecosystem—including data, applications, agents, APIs, infrastructure, users, and third-party providers.
