How enterprises adopt, use, and adapt artificial intelligence (AI) Artificial intelligence is starting to play a significant role in how organizations do business, make decisions, deliver experiences, and protect themselves against threats. It’s happening faster than ever.
On the other hand, the quick assimilation of AI into production creates fresh security risks. Sensitive data leaks can occur through AI systems, models can be tampered with, and AI can be used by malicious actors to accelerate cyberattacks.
Traditional cybersecurity controls alone are not always sufficient to address these risks. Enterprises need structured AI security frameworks that help them identify, assess, manage, and continuously monitor AI-related risks.
Here are some of the key frameworks and approaches enterprises should consider adopting.
1. NIST AI Risk Management Framework
The NIST AI Risk Management Framework (AI RMF) is one of the most widely recognized approaches for managing AI risks.
The framework, designed by the U.S. National Institute of Standards and Technology, offers a set of customizable software tools and guidance to help industry, academia, government and other organizations adopt a technology that is both secure and trustworthy.
The framework is built around four core functions:
- Govern
- Map
- Measure
- Manage
Enterprises can use these principles to identify potential AI risks, evaluate their impact, establish accountability, and implement appropriate controls.
The framework is particularly useful for organizations looking to build an enterprise-wide AI governance program rather than treating AI security as an isolated technical issue.
2. NIST Cybersecurity Framework
The CSF is specifically intended to be used to complement AI-focused governance. The CSF delivers a ready-made set of best practices for cybersecurity to secure infrastructure around AI systems.
Organizations can apply cybersecurity principles such as:
- Identify critical AI assets and data
- Protect AI infrastructure and access
- Detect suspicious activity
- Respond to AI-related security incidents
- Recover affected systems
For example, an enterprise deploying an internal generative AI platform can use the framework to secure user identities, cloud infrastructure, APIs, databases, endpoints, and monitoring systems supporting the AI environment.
Combining AI risk management with established cybersecurity controls creates a more comprehensive security strategy.
3. ISO/IEC 42001
ISO/IEC 42001 is an international standard dedicated to for Artificial Intelligence Management Systems (AIMS). This standard guides organizations on the step-by-step process of designing, implementing, maintaining, and iteratively improving AI management systems.
The standard focuses on areas such as:
- AI governance
- Risk management
- Accountability
- Transparency
- Data management
- Impact assessment
- Continuous improvement
For multinational enterprises, ISO/IEC 42001 can be particularly valuable because it provides a globally recognized framework for demonstrating that AI systems are being managed responsibly.
Organizations can also integrate the standard with existing ISO management systems and information security practices.
4. OWASP Top 10 for LLM Applications
Generative AI and large language models introduce security risks that traditional application-security frameworks may not fully address.
The OWASP Top 10 for LLM Applications offers security teams a practical tool for visualizing the most common threats encountered when working with LLM applications.
Key risks include:
- Prompt injection
- Sensitive information disclosure
- Supply chain vulnerabilities
- Data and model poisoning
- Improper output handling
- Excessive agency
- System prompt leakage
- Vector and embedding weaknesses
- Misinformation
- Unbounded consumption
Enterprises developing AI copilots, chatbots, AI agents, or retrieval-augmented generation (RAG) applications should incorporate these risks into their application security lifecycle.
5. MITRE ATLAS
MITRE ATLAS A knowledge base that helps defenders understand how attackers target machine-learning systems and AI-enabled applications. ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a knowledge base that helps defenders understand how attackers target machine-learning systems and AI-enabled applications.
Organizations can use ATLAS to:
- Model AI-specific attack techniques
- Conduct threat assessments
- Build adversarial testing scenarios
- Improve detection capabilities
- Develop incident-response strategies
This is especially important for companies deploying AI in high value settings where misappropriation of an AI system could result in large operational or financial impact.
6. Zero Trust for AI Systems
Zero Trust should also become an important component of enterprise AI security.
The point is simple: don’t ever assume users, applications, devices, models, or AI agents are automatically trustworthy just because they are within your organization.
Enterprises should implement controls such as:
- Strong identity verification
- Least-privilege access
- Continuous authentication
- Role-based permissions
- Segmentation
- API security
- Continuous monitoring
AI agents deserve particular attention because increasingly autonomous systems may be capable of accessing databases, applications, files, and business workflows.
Their permissions should therefore be tightly controlled and continuously evaluated.
7. Secure AI Development Lifecycle
Just frameworks don’t suffice. A comprehensive AI security assessment during the entire development lifecycle of AI. The following security assessments should be part of the secure AI development lifecycle:
Planning: Identify potential threats, regulatory requirements, data risks, and business impacts.
Development: Secure training data, code, models, APIs, dependencies, and development environments.
Testing: Conduct vulnerability assessments, adversarial testing, prompt-injection testing, and model evaluations.
Deployment: Apply strong authentication, authorization, monitoring, logging, and infrastructure security.
Operations: Continuously monitor model behavior, access patterns, data usage, and emerging threats.
Retirement: Securely remove models, credentials, datasets, integrations, and sensitive information when systems are decommissioned.
This approach ensures security is not added as an afterthought.
8. AI Supply Chain Security
Businesses are more dependent on third-party AI provider, open-source models, pretrained models, datasets, APIs, cloud providers, and other third-party AI applications than ever.
This creates a significant AI supply-chain risk.
Organizations should maintain visibility into:
- Where AI models originate
- What datasets are being used
- Which third-party services are connected
- What open-source components are included
- Who can modify models or datasets
- Where sensitive information is processed
AI vendors should also be evaluated as part of the organization’s third-party risk-management program.
9. Data Governance and Privacy Controls
All in all, AI security is at the mercy of the data security. Organizations should adopt stringent policies on what information should be fed into AI engines.
Sensitive data such as customer records, intellectual property, financial information, credentials, and confidential business documents should be protected through appropriate controls.
Important measures include:
- Data classification
- Encryption
- Data-loss prevention
- Access controls
- Retention policies
- Data masking
- Audit logging
- Privacy assessments
Employees should also understand what information they can and cannot enter into public or enterprise AI tools.
10. Build an AI Security Governance Program
The best of all possible worlds: Don’t do one on your own. Companies need to assemble an encompassing AI security program that integrates governance, cybersecurity, application security, privacy, and ongoing monitoring.
A practical structure could look like this:
Security AreaRecommended ApproachAI GovernanceNIST AI RMF / ISO 42001CybersecurityNIST CSFLLM SecurityOWASP Top 10 for LLM ApplicationsAI Threat ModelingMITRE ATLASIdentity & AccessZero TrustDevelopmentSecure AI Development LifecycleData ProtectionData Governance & Privacy ControlsThird-Party RiskAI Supply Chain SecurityMonitoringContinuous AI Security Monitoring
Why Enterprises Need a Multi-Framework Approach
No single framework addresses every AI security challenge.
An enterprise may use ISO/IEC 42001 to establish AI governance, NIST AI RMF to manage AI risks, NIST CSF to strengthen cybersecurity controls, OWASP guidance to secure LLM applications, and MITRE ATLAS to understand adversarial AI techniques.
Together, these approaches create a more complete security architecture.
The goal should not simply be framework compliance. The objective is to create an environment where AI systems can be deployed responsibly while maintaining security, privacy, accountability, and business resilience.
Final Thoughts
AI is taking center stage in enterprise technology, yet AI-driven enterprise applications broaden an organization’s attack surface. When deployed without proper protections, the use of AI may leave you vulnerable to data leakage, prompt injection, model poisoning, supply-chain attacks, rogue AI-agent actions, and/or regulatory capture.
Adopting established frameworks such as NIST AI RMF, ISO/IEC 42001, NIST CSF, OWASP guidance, and MITRE ATLAS can provide organizations with a strong foundation for managing these risks.
The coming era of enterprise AI won’t be about the strength of a given organization’s AI systems. It will be about their security, ethics, and resilience.
For enterprises, AI security should be treated as a core business capability — not an optional layer added after deployment.
