What Is an AI Security Assessment?
An AI security assessment is the systematic evaluation of artificial intelligence systems, models, data pipelines, and infrastructure to identify vulnerabilities, assess risks, and implement appropriate security controls. AI security evaluation examines unique attack surfaces created by machine learning models, requiring specialized approaches for model-specific threats.
An effective AI security assessment covers four critical domains:
- AI models: algorithms, weights, decision logic
- Training and inference data: sources, lineage, integrity
- Supporting infrastructure: GPUs, cloud services, APIs
- Governance processes: compliance, change management
AI systems are dynamic entities that evolve through training cycles. They process vast amounts of often unvetted data and create new attack vectors like adversarial examples, data poisoning, and prompt injection that require specialized detection methods.
Modern AI security assessment frameworks align with established standards like the NIST AI Risk Management Framework and ISO/IEC 42001, providing structured methodologies that satisfy both AI security audit requirements and regulatory compliance needs.
Why an AI Risk Assessment Matters Now
The urgency for comprehensive AI security evaluation continues to grow. Phishing attacks increased by 1,265% driven by the growth of generative AI, and 40% of all email threats are now AI-enabled, demonstrating the need for specialized defenses against model-specific risks.
Regulatory pressure is also mounting. The EU AI Act and proposed U.S. executive orders on AI safety require organizations to demonstrate comprehensive risk management and security controls. Draft standards like ISO/IEC 42001 establish management system requirements for AI security, pushing organizations to prove governance across the AI lifecycle.
AI-specific threats continue to evolve:
- Model poisoning attacks manipulate training datasets to alter model behavior, creating security breaches that remain dormant until triggered
- Prompt injection attacks against large language models can bypass security hardening with malicious requests, potentially exposing sensitive data
- Adversarial examples subtly alter inputs to cause misclassification
- Model extraction uses API queries to reconstruct intellectual property
The business impact includes technical vulnerabilities and operational risks. A compromised AI system in healthcare could affect patient diagnoses, while poisoned financial models might enable fraud or create regulatory violations. Supply chain risks through compromised pre-trained models or tainted datasets can propagate vulnerabilities across multiple systems.
Organizations implementing proactive AI vulnerability assessment programs position themselves to use AI safely while maintaining competitive advantages and stakeholder trust. Regular AI risk assessment cycles help organizations identify emerging threats before they become critical vulnerabilities.
A 6-Phase AI Security Assessment Framework
A systematic AI security assessment employs a six-phase cycle inspired by the NIST AI Risk Management Framework and ISO/IEC 42001's Plan-Do-Check-Act approach. Each phase builds continuously as your AI systems evolve.
Phase 1: Define scope and objectives establishes assessment boundaries, risk tolerance, and success criteria. This maps to NIST's "Govern" function and ISO's context establishment requirements.
Phase 2: Inventory AI assets and data flows catalogs every model, dataset, pipeline, and dependency, creating your defensible system of record with metadata like training data lineage and model versions.
Phase 3: Threat mapping and vulnerability analysis probes each asset using adversarial AI techniques, referencing MITRE ATLAS for threat models like model poisoning and prompt injection. This AI vulnerability assessment phase identifies attack vectors specific to machine learning systems.
Phase 4: Score and prioritize AI risks creates a likelihood-impact matrix weighted by business context and regulatory requirements, producing an AI risk register for executive review and AI security audit documentation.
Phase 5: Implement controls and mitigation deploys specific safeguards like input validation, access governance, and adversarial hardening.
Phase 6: Reporting, validation, and continuous monitoring generates auditable reports, validates fixes through testing, and establishes ongoing telemetry.
Step-by-Step AI Security Assessment Implementation Guide
Phase 1: Define scope and objectives
Begin with three critical inputs: business drivers (AI value and failure impact), regulatory landscape (HIPAA, GDPR mandates affecting AI), and organizational AI maturity (existing documentation and governance).
Create a one-page assessment charter including:
- Project purpose
- Scope boundaries
- Success criteria
- Timeline
- Accountability through RACI matrices
Draw clear assessment boundaries by separating production models from research, mapping data pipelines, and listing external dependencies. Define risk tolerance with concrete criteria like "no PII in model outputs" or "accuracy drops >3% trigger rollback." Without clear boundaries, scope creep slows progress and dilutes assessment quality. Time-box each phase and assign RACI accountability to maintain momentum.
Position the assessment as a quality enabler rather than a roadblock. When product teams view security assessments as obstacles, resistance undermines implementation. Frame assessments as protecting AI initiatives from larger problems that could delay launches or damage reputation.
Phase 2: Inventory AI assets and data flows
Build a comprehensive AI asset register listing all models, their architecture, training data sources, versions, and deployment information. Document model cards, lineage information, and licensing terms. Incomplete asset discovery creates blind spots when data science teams deploy "shadow models" outside change control. Run quarterly discovery scans and reconcile against master inventories to maintain accurate visibility.
Track data flows using discovery tools like SBOM scanners and cloud asset managers. Platforms like the SentinelOne Singularity Platform provide comprehensive visibility across AI infrastructure, automatically discovering and cataloging AI assets.
Implement verification processes including cross-checking against known datasets and automated discrepancy detection. Build lineage checks into asset registers and attach license artifacts to datasets. Training data IP issues pose legal risks if data provenance cannot be proven, so document the full chain of custody for all training data.
Address supply chain vulnerabilities by requiring SBOMs from vendors for pre-trained models and libraries. Pin model versions to cryptographic hashes to prevent tampering and ensure reproducibility.
Phase 3: Threat mapping and vulnerability analysis
Anchor threat analysis to MITRE ATLAS, which extends ATT&CK with AI-specific tactics. Focus on four critical threats:
- Model poisoning: tainted training data with hidden backdoors
- Prompt injection: malicious inputs overriding system instructions
- Adversarial examples: subtle input alterations causing misclassification
- Model extraction: API queries reconstructing intellectual property
Establish AI-focused red teams that enumerate relevant ATLAS techniques, generate attack playbooks, and combine automated fuzzing with manual testing.
Phase 4: Score and Prioritize AI Risks
Plot identified risks on likelihood-impact matrices considering AI-specific factors: bias, model drift, explainability, and adversarial robustness. Create risk registers including:
- Risk ID
- Affected assets
- Threat scenarios
- Scores
- Mitigation owners
- Monitoring KPIs
Phase 5: Implement Controls and Mitigation
Sort controls by implementation effort and security payoff. Quick wins include prompt validation, API rate limiting, and verbose logging. Medium-complexity controls involve role-based access and automated lineage tracking.
Tailor controls to technology stacks:
- For LLMs: input sanitization and output moderation
- For vision systems: adversarial patch detection and sensor fusion
- Universal controls: encryption, least-privilege access, and real-time telemetry streamed to security platforms
Advanced solutions with Purple AI capabilities provide natural language security analysis and automated threat hunting designed for AI environments.
Phase 6: Reporting, Validation, and Continuous Monitoring
Create documentation with executive summaries in business terms plus technical reports detailing methodologies. Use risk visualizations like heat maps for stakeholder communication.
Validate through purple-team exercises and tabletop scenarios. False completion happens when teams check boxes without validating controls actually work. Schedule red-team validation before final reports to confirm that implemented controls perform as expected under real attack conditions.
Establish quarterly reviews with automated alerts. Integrate AI security monitoring with existing security operations using unified endpoint security platforms across technology stacks.
Strengthen your AI Security Foundation
If you’re struggling with your current AI security assessments and want to change how things work, then SentinelOne can help. Using the right tools, technologies, and workflows is just as important as finding and mitigating known and unknown vulnerabilities. SentinelOne can give you a clear roadmap on how to manage AI security risks by starting off with a cloud security audit.
You can use Singularity™ Cloud Security to verify exploitable risks and stop runtime threats. It’s an AI-powered CNAPP that can give deep visibility into your current AI security posture. AI-SPM can help you discover AI models and pipelines. You can even configure checks on AI services and run automated pen tests with its External Attack and Surface Management (EASM) feature. Purple AI conducts autonomous investigations and threat hunting, while Storyline™ technology reconstructs complete attack narratives for thorough validation. SentinelOne’s Offensive Security Engine™ can thwart attacks, predict new moves, and map progressions. You can prevent attacks before they happen and prevent escalations in your AI infrastructure.
SentinelOne’s container and Kubernetes Security Posture Management can also do misconfiguration checks. SentinelOne’s Prompt Security agent is lightweight and can provide model-agnostic coverage for major LLM providers like Google, Anthropic, and Open AI. It can secure your infrastructure from prompt injection, model data poisoning, malicious prompts, model misdirection, and other kinds of prompt-based AI security threats. You can auto-block high-risk prompts, eliminate content bypass filters, and thwart jailbreak attacks.
You also get real-time monitoring and policy enforcement for AI activities taking place in your APIs, desktop apps, and browsers. Prompt Security also helps with managing your AI services and empowers MSSPs to detect anomalies and enforce AI security policies more effectively.
SentinelOne ensures secure AI deployments and aligns with regulatory frameworks like the NIST AI Risk Management Framework and the EU AI Act. Singularity™ XDR Platform can connect security data from endpoints, cloud workloads, and identities, to give you a full view of all AI-related threats. You can use SentinelOne's AI engine to take automated action to contain threats once you detect them and mitigate risks posed to AI systems. SentinelOne's Vigilance MDR Service also provides 24/7 human expertise and threat hunting services to find and neutralize various AI-related threats and risks.
Sign up for a customized demo with SentinelOne to see how our AI-powered total protection can help you outpace quickly changing threats.
Conclusion
AI security assessments will be valuable to your organization and gain more importance as you adopt more AI models, services, and other kinds of features. Almost every company is integrating AI into their workflows these days, so you definitely don't want to fall behind. But while you are adopting increasing AI usage, you also want to make sure that whatever services and tools you adopt are secure. SentinelOne's products and services can help you make better AI security assessments. If you have any doubts or need more clarity, you are welcome to reach out to our team and get your queries answered.
FAQs
Conduct comprehensive AI security assessments quarterly for production systems, with monthly automated scans for asset discovery and vulnerability detection. High-risk systems serving critical business functions may require more frequent evaluation cycles. Trigger immediate reassessments whenever major model updates occur, new data sources integrate, or significant architecture changes deploy. Organizations in regulated industries should align assessment frequency with compliance audit schedules.
The NIST AI Risk Management Framework offers comprehensive governance guidance for managing AI risks across the full lifecycle. MITRE ATLAS provides tactical threat intelligence specifically focused on adversarial machine learning attacks. ISO/IEC 42001 addresses management system requirements for responsible AI development and deployment. The OWASP LLM Top 10 covers language model-specific vulnerabilities. Organizations should combine multiple frameworks based on their specific AI use cases and regulatory requirements.
AI security assessments examine dynamic, learning systems that require specialized techniques beyond traditional penetration testing methods. While traditional testing focuses on static code vulnerabilities and network security, AI assessments evaluate model behavior under adversarial conditions, data poisoning scenarios, and prompt injection attacks. AI assessments must account for training data integrity, model drift detection, and inference-time vulnerabilities that don't exist in conventional software. Traditional security tools cannot detect threats like backdoored models or adversarial examples.
CISOs should prioritize regulatory compliance failures that could result in substantial penalties under emerging AI regulations like the EU AI Act. Intellectual property theft through model extraction represents significant competitive risk. Reputational damage from biased or inappropriate AI outputs can harm customer trust and brand value. Operational disruption from poisoned models affecting critical business decisions poses immediate business continuity risks. Supply chain vulnerabilities in third-party AI components require careful vendor risk management.
Quantify potential breach costs by calculating the financial impact of AI system compromises on business operations and customer data. Document regulatory penalties avoided through proactive compliance with AI security standards. Track reduced incident response times and security team efficiency gains from automated AI security monitoring. Measure competitive advantages gained from secure AI deployment that enables innovation while competitors face security setbacks. Present case studies showing how AI security assessments prevented real attacks or identified critical vulnerabilities before exploitation.
