AI vs. Conventional IT Controls: How Security, Compliance, and Risk Management Are Evolving

 

Information technology has always been a rapid changing field. IT controls exist to play a crucial role in safeguarding systems, data, and processes. With the advent of artificial intelligence (AI) and its integration into IT environments, organizations must adjust the conventional control frameworks to account for AI’s unique risks and operational characteristics. This article explores the differences in how conventional IT controls and AI-specific controls are implemented and measured, highlighting key challenges and best practices. 

It is important to highlight that is a high-level approach to understanding the key differences between conventional IT and AI controls. The controls discussed below are the core controls in IT environments, not the totality of existing and applicable controls.

1. Access Controls

Conventional IT access controls rely on principles like least privilege and RBAC, enforced through systems like IAM and MFA. These methods restrict access to systems and data based on users’ roles and responsibilities. In AI, access controls extend to models and datasets, where the risk of unauthorized manipulation or misuse of models and sensitive training data adds complexity. Conventional audits may suffice for IT environments, but AI environments require additional controls such as securing APIs, restricting access to AI models, and monitoring who can influence training inputs.

2.   Change Management Controls

Change management in conventional IT is well-documented, following frameworks like ITIL to manage software and infrastructure updates through formal approval processes and versioning. For AI, change management becomes more nuanced, with the need to manage changes not just in code but in training data, model weights, and algorithms. While IT changes are measured through impact and rollback effectiveness, AI changes are measured via model drift, data integrity, and fairness metrics, requiring continual evaluation.

3.   IT Operations Controls

Conventional IT operations focus on uptime, capacity planning, system backups, and patching. These controls aim to keep systems running efficiently and securely. In AI, IT operations transform into MLOps, where AI-specific tasks like automated model deployment, continuous retraining, and data pipeline monitoring are essential. Performance metrics like CPU load and system uptime remain relevant, but AI adds new KPIs such as model inference latency, accuracy degradation, and frequency of retraining cycles.

4.   Security Controls

Security controls in IT aim to defend against malware, unauthorized access, and data breaches using firewalls, IDS/IPS, and antivirus software. While these tools remain necessary for AI environments, AI systems also face threats like adversarial attacks, data poisoning, and model inversion. Protecting AI requires new strategies, including securing training data, using robust model architectures, and performing stress testing against adversarial inputs—risks not typically addressed in conventional security frameworks.

5.   Logging and Monitoring Controls

In conventional IT, logging and monitoring focus on system events, network traffic, and user activity to detect and respond to anomalies. In AI systems, logging must include not only infrastructure logs but also model decisions, data inputs, and output rationale. Explainability becomes part of monitoring, and tools like SHAP or LIME are employed to interpret model behavior. Conventional SIEM systems may need customization to handle the increased volume and complexity of AI logs.

6.   Incident Management Controls

Conventional incident management uses structured playbooks to detect, contain, and recover from cyber incidents. Metrics such as MTTD and MTTR gauge the effectiveness of these processes. In AI, the incident landscape includes model failures, hallucinations, ethical issues, and unexpected behaviors. Organizations must develop AI-specific incident plans, which include automated model rollbacks, retraining procedures, and forensic reviews of input data and model output logs.

7.   Compliance and Risk Management Controls

Compliance in IT typically involves aligning with established frameworks like HIPAA, SOX, and ISO 27001. Risk management entails regular assessments, controls testing, and documentation. In AI, compliance introduces newer regulatory landscapes like the EU AI Act, and frameworks like the NIST AI RMF. Risk management must now assess bias, explainability, and ethical implications. AI requires dynamic risk registers, updated continuously with metrics around model transparency, fairness, and legal accountability.

8.   Business Continuity & Disaster Recovery (BC/DR) Controls

Conventional BC/DR ensures systems and data can be restored after disasters using offsite backups, redundant systems, and recovery testing. For AI, BC/DR must address the availability and recoverability of training datasets, models, and pipelines. AI recovery involves automated redeployment of fallback models, retraining from backup datasets, and ensuring minimal disruption in AI-dependent decision-making processes. Recovery metrics evolve from RTOs and RPOs to include AI-specific measures like model restoration time and decision accuracy post-recovery.

 

The Future of AI Controls

As AI continues to reshape IT landscapes, organizations must evolve their control frameworks to ensure security, compliance, and operational integrity. The future of AI controls will likely involve:

• Greater regulatory oversight with evolving AI compliance standards.
• Enhanced AI explainability to address ethical and transparency concerns.
• Improved AI-driven security tools to proactively defend against adversarial attacks.
• Automated AI governance frameworks for continuous risk assessment and compliance monitoring.

Conclusion

Conventional IT controls provide a foundation for security, compliance, and risk management; however, AI-driven environments demand additional controls to address model integrity, transparency, and ethical considerations. Organizations adopting AI must extend their IT governance frameworks to include AI-specific monitoring, security, and compliance measures to ensure responsible and effective AI deployment. Businesses that embrace AI-specific controls early on and continuously refine them can strike the right balance between innovation and risk management, ensuring AI remains a valuable and secure asset in modern IT ecosystems and not a vector threat.