What are AI Security Standards?
AI security standards are comprehensive frameworks that provide structured approaches for identifying, assessing, and mitigating the unique risks associated with artificial intelligence systems. AI security frameworks address the entire machine learning lifecycle, from training data integrity to model deployment and ongoing monitoring.
The large number of AI security frameworks and AI governance standards has created what many describe as compliance chaos for Chief Information Security Officers. With new mandates like the EU AI Act, Executive Orders on AI, and various state regulations demanding attention, CISOs face challenges that go beyond traditional security concerns.
Each AI security framework comes with its own set of standards and expectations, adding layers of complexity that can hinder effective decision-making. A unified strategy based on your organization's risk tolerance and specific regulatory pressures can bring some order to the chaos. By seeking alignment between overlapping areas and standardizing approaches, you can create a more cohesive security policy that manages AI risks effectively.
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Why AI Security Requires New Frameworks
Traditional security architecture was designed for deterministic software that behaves the same way every time given the same input. Machine learning models are probabilistic by nature; the same prompt can produce wildly different outputs, which can reveal sensitive data or impact downstream systems in unexpected ways.
The attack surface expansion
AI use introduced a new and larger attack surface that has to be protected. You now defend training data that adversaries can poison, model weights an insider can exfiltrate, inference endpoints vulnerable to prompt injection and denial-of-service, and the fragile human-AI interaction layer where overreliance creates risky automation loops. Each of these attack vectors operates differently from traditional software vulnerabilities.
The NIST AI Risk Management Framework captures this uncertainty by addressing risks associated with 'model behavior' within its broader risk management activities, a dimension that classic vulnerability scanners have traditionally overlooked.
Supply chain complexity
Google's Secure AI Framework (SAIF) formalizes these new choke points, placing "secure AI supply chain" alongside detection and response. That supply chain now stretches beyond source code to include public datasets scraped from the internet, pre-trained foundation models pulled from open repositories, and third-party orchestration tools..
A single compromised dependency can corrupt every model retrained downstream, compounding the potential impact of each risk. Securing AI means continuously measuring, testing, and governing a system whose behavior evolves over time, sometimes in ways you can't predict until it fails in production.
5 Essential Frameworks for AI Security Standards
The landscape of AI security standards is complex, with various frameworks designed to address different facets of artificial intelligence compliance and risks. Understanding which frameworks to prioritize can mean the difference between a robust security policy and drowning in compliance requirements.
1. OWASP LLM Top-10: Your starting point for AI compliance
For teams leveraging large language models, the OWASP LLM Top-10 focuses on the ten most critical vulnerabilities that attackers exploit in LLM applications, addressing issues like prompt injection and supply chain vulnerabilities.
Why start here: Implementation is feasible within weeks, offering rapid response to emerging threats. The framework provides concrete, actionable guidance rather than abstract principles.
SentinelOne Integration: Tools like Purple AI can detect OWASP attack patterns in real-time, providing immediate insights into incidents associated with LLM01 (Prompt Injection) and LLM05 (Supply Chain) vulnerabilities.
2. NIST AI RMF 1.0: Your regulatory insurance policy
The NIST Risk Management Framework establishes a governance structure that becomes essential for regulatory compliance. Its strength lies in mapping regulatory demands across different jurisdictions and providing a common language to discuss artificial intelligence compliance and risk.
Implementation challenge: The framework relies on a catalog of over 1,000 controls (NIST SP 800-53), which can be overwhelming. The key is focusing on 20% of the controls that mitigate 80% of your risk..
3. MITRE ATLAS: Understanding your adversaries
MITRE ATLAS supports threat modeling specific to artificial intelligence systems by mapping adversarial tactics and providing a comprehensive view of potential threats. It's particularly valuable for red team exercises and threat hunting activities.
Real-world application: Attack techniques like data poisoning documented within ATLAS are now surfacing in production environments, making this framework useful for understanding current threat landscapes.
Detection capabilities: SentinelOne's behavioral analysis capabilities can detect ATLAS tactics beyond typical signature-based tools, offering advanced protection against sophisticated AI-targeted attacks.
4. Google SAIF: Enterprise-grade supply chain security
Google's Secure AI Framework represents an enterprise-level approach designed to safeguard the entire AI lifecycle from development to deployment. While comprehensive, it requires significant investment in tooling and processes.
Key strengths: Pillars like "Secure AI supply chain" and "Monitor AI behavior" offer practical starting points for implementation, especially for organizations already using cloud-based AI services.
Integration opportunity: When used alongside SentinelOne's security capabilities, SAIF provides complementary protection across various stages of AI deployment.
5. ISO/IEC 42001: When certification matters
ISO/IEC 42001 positions itself as a certifiable management system for artificial intelligence compliance and security, crucial for industries requiring strict compliance documentation like financial services, healthcare, and government contracts.
Implementation reality: The 12-18 month certification process involves substantial documentation and organizational commitment. For compliance-driven organizations, a strategic approach involves building capabilities with other frameworks first, then mapping them to ISO for formal certification.
Strategic timing: Start the ISO process after establishing operational security controls through other frameworks to avoid lengthy certification cycles without practical security improvements.
How to Implement AI Security Standards
Trying to rapidly implement every control for one or more AI security standards guarantees burnout. Here's a 6-month plan that delivers quick wins while building the discipline auditors expect.
Address critical AI security risks (Month 1)
Begin with patching the biggest holes first. Apply OWASP LLM Top-10 mitigations including prompt sanitization, output filtering, and strict dependency pinning. Deploy continuous data collection from your endpoints into SentinelOne Singularity so Purple AI surfaces prompt-injection and data-exfiltration attempts in real time.
Create a living asset inventory using the NIST framework's Map function template for documenting models, datasets, and third-party services. This inventory becomes the foundation for all subsequent security activities.
Build the foundation (Months 2-3)
Establish a governance committee aligned with AI governance standards with a clear RACI matrix so security, data science, legal, and product teams all own their part of the risk. Use MITRE ATLAS techniques to threat-model each critical workflow. This exercise often uncovers data-poisoning paths that traditional reviews miss.
With risks identified, instrument baseline metrics under the NIST framework's "Measure" function to track drift, bias, and adversarial robustness. These metrics provide objective evidence of your security policy improvements.
Scale and systematize (Months 4-6)
Address supply chain risks by aligning with Google SAIF's "Secure development" and "Monitor behavior" pillars. Embed automated controls like anomaly detection into existing CI/CD or MLOps pipelines so every new model ships with consistent guardrails.
If your industry demands formal proof, begin an ISO 42001 gap analysis now. The earlier phases supply 80% of the evidence auditors need, making certification a documentation exercise rather than a security overhaul.
Improve Your AI Security Program
AI security standards have changed the way we approach cybersecurity and use AI models and services. Every organization uses its own specific AI security framework which means they have their own unique challenges to deal with. Modern compliance standards like NIST AI RMF, OWASP LLM Top-10, and Google SAIF have created both opportunities and complexity for security teams.
Purple AI continuously improves their threat detection and response capabilities. This gen AI cybersecurity analyst can learn from incidents of today, extract insights, analyze events, and inform to prepare for the threats of tomorrow.
SentinelOne can predict threats and learn how they work before they can launch attacks and escalate issues in your organization. Its unique offensive security engine with verified expert paths can map and correlate findings. You can use SentinelOne's threat intelligence to update your AI security program, find out current weaknesses and address them. SentinelOne's AI security portfolio can up-level your AI security posture. Its agentless CNAPP can help you improve your AI security posture and help with AI security posture management by discovering your latest AI models, pipelines and services.
SentinelOne's Prompt Security Agent is lightweight and it provides model agnostic security coverage for major LLM providers like Open AI, Google and Anthropic. You can use the agent to prevent AI data poisoning attacks, model manipulation, and prevent malicious prompts from being written or misdirecting models. SentinelOne can also improve your AI security compliance and help you stay up to date with the latest standards. It helps you adhere to AI ethics and ensure that you responsibly use all AI models and services. It applies the strictest guardrails and doesn’t use user data for training models.
Request a demo to see how SentinelOne's AI-powered platform can help you implement these frameworks and protect against emerging AI threats.
Conclusion
If you're struggling to know which AI security standards are right for you, then we recommend doing a security audit first on your current infrastructure. Learn more about your business requirements, use cases, and how exactly your AI security program would fit in. It will help you adhere to the best standards accordingly and make sure you stick to the right ones. If you need a consultation, we can help. Be sure to reach out to our team.
FAQs
If you're shipping or consuming large language model features, begin with the OWASP LLM Top-10 for immediate vulnerability coverage. Otherwise, stand up the NIST framework's "Map" and "Measure" functions to create a risk baseline you can iterate on. Quick wins build momentum for longer-term governance initiatives.
Tie spending to avoid losses. Skills gaps and unmanaged AI projects drive costly incidents and audit findings, yet disciplined framework adoption measurably lowers both risks and remediation timeframes. Present frameworks as insurance policies that pay dividends through reduced incident costs and faster regulatory compliance.
Partially. Traditional XDR and endpoint protection still stop commodity malware, but AI-specific attacks hide in business logic that conventional tools miss. You need behavioral analytics and model-aware monitoring to catch threats like prompt injection, model extraction, and training data poisoning.
Inventory your AI assets and apply input/output filtering for the OWASP LLM Top-10. Document risks using NIST's one-page profiling worksheet. This combination provides substantial coverage with minimal investment, creating a foundation for future expansion.
AI governance standards help bridge the gap between traditional compliance requirements and AI-specific risks. Elements of ISO 27001 controls (access management, logging, incident response) already align well with NIST and SAIF requirements. Keep evidence in the same audit repository to avoid duplicate documentation efforts. Focus new work on AI-specific controls like model monitoring and algorithmic auditing.
