Executive Summary
As Artificial Intelligence (AI) becomes integral to enterprise operations, it carries unique risks beyond traditional IT systems. While frameworks such as NIST RMF (Risk Management Framework) provide structured approaches for authorization and security, AI systems require additional tailored controls to mitigate threats such as data poisoning, adversarial attacks, and biased decision-making. This white paper outlines AI-specific controls, their integration with existing security standards, and actionable recommendations for enterprises.
Introduction
Organizations are accelerating the deployment of AI systems to optimize workflows, automate decisions, and drive innovation. However, AI systems are fundamentally different from traditional software:
- They learn from data, inheriting data biases and quality issues.
- They are susceptible to model drift as real-world conditions change.
- They are vulnerable to adversarial attacks specifically designed to manipulate model behavior.
Traditional IT controls, while necessary, are insufficient on their own. AI-specific controls must complement standard security, privacy, and compliance measures to safeguard enterprises against novel risks introduced by AI technologies.
Why AI-Specific Controls Matter
Unlike deterministic software, AI systems produce outputs based on probabilistic models, which:
- May behave unpredictably with unfamiliar inputs.
- Can be exploited through subtle data manipulations.
- Can encode biases from training data into decisions affecting customers, employees, and business outcomes.
Moreover, regulators are increasingly scrutinizing AI systems. The EU AI Act, emerging US AI accountability guidelines, and sector-specific requirements (e.g. financial services model risk management) are mandating organizations to implement controls that ensure transparency, fairness, and robustness.
Key AI-Specific Controls
1. Data Controls
a. Data Provenance and Lineage
Track the origin, licensing, and transformation steps for training and testing data. This ensures:
- Legal compliance (e.g. with data usage licenses).
- Trust in model inputs.
- Auditability for downstream decisions.
b. Data Quality Validation
Implement automated checks for data accuracy, completeness, and consistency before ingestion into AI models to prevent garbage-in-garbage-out risks.
c. Data Minimization & Privacy Enhancements
Limit data collection to what is strictly required. Apply differential privacy or anonymization techniques to protect personal data within training datasets.
2. Model Controls
a. Model Integrity
Use cryptographic signatures and hashes for model files to prevent tampering between training, testing, and deployment environments.
b. Version Control for Models
Track versions of models and training pipelines. This supports rollback, reproducibility, and auditing in regulated environments.
c. Bias & Fairness Assessments
Conduct pre-deployment and periodic audits for:
- Disparate impact on protected groups.
- Statistical fairness metrics (e.g. demographic parity, equalized odds).
Implement remediation strategies such as re-sampling, re-weighting, or algorithmic adjustments.
d. Explainability & Transparency
Deploy explainability tools (e.g. SHAP, LIME) for models used in high-stakes decisions (e.g. credit scoring, hiring). Document model logic and decision pathways for stakeholders and auditors.
e. Adversarial Robustness Testing
Test models against adversarial inputs specifically crafted to exploit vulnerabilities. Implement defenses such as adversarial training or input sanitization.
f. Continuous Performance Monitoring
Monitor models for:
- Data drift: Changes in input data distributions.
- Concept drift: Changes in the relationship between input and output.
Trigger retraining or human intervention when performance deteriorates.
3. Access and Authorization Controls
a. Role-Based Access Control (RBAC)
Define granular permissions for AI pipeline components:
- Data ingestion and labeling.
- Model training and validation.
- Deployment and monitoring.
Prevent privilege creep among data scientists, MLOps engineers, and DevOps personnel.
b. Privileged Access Management
Enhance controls over accounts with elevated permissions to retrain or redeploy models, given the potential for intentional or accidental model manipulation.
4. Supply Chain Controls
a. Third-Party Model Vetting
Assess external models for:
- Licensing compliance.
- Embedded malicious code or data leakage risks.
- Alignment with internal security and fairness standards.
b. Library and Framework Integrity
Verify the integrity and update status of AI frameworks (e.g. TensorFlow, PyTorch) to mitigate vulnerabilities in underlying libraries.
5. Deployment and Operational Controls
a. Shadow Deployment (Canary Testing)
Deploy models in shadow mode to observe real-world behavior alongside existing systems before full production release.
b. Audit Logging
Implement robust logging for AI systems:
- Inputs and outputs.
- Model version and configuration used.
- Decision rationale or explainability outputs where feasible.
This supports incident investigation, compliance audits, and model accountability.
6. Incident Response Controls
Integrate AI-specific scenarios into Incident Response (IR) playbooks, including:
- Data poisoning attacks altering model behavior.
- Adversarial exploitation leading to incorrect decisions.
- Unauthorized model modification or deployment.
Ensure security operations teams are trained to recognize and respond to these AI-related incidents.
Recommendations for Enterprise Implementation:
Integrate AI Controls into RMF Lifecycle
- Extend security categorization to include model impact analysis.
- Tailor control baselines to incorporate AI-specific controls.
Establish AI Governance Policies
- Define organization-wide standards for AI ethics, explainability, and robustness.
Upskill Security Teams
- Train cybersecurity and risk professionals on AI vulnerabilities and control implementation.
Continuous Improvement
- Periodically review AI controls in light of evolving threats and regulatory guidance.
Conclusion
AI promises significant operational advantages, but without AI-specific controls, it exposes enterprises to novel risks that traditional IT controls fail to address. Organizations should integrate these controls into their NIST RMF processes and CGRC-aligned practices to ensure secure, fair, and compliant AI deployments.
About the Author
Andre Spivey is a SecOps and Network Engineering and Change Manager and founder of Global Cyber Education Forum, specializing in AI and cybersecurity governance. His work bridges technical implementation with strategic risk management for enterprise and government clients.