Artificial intelligence has transitioned from a research experiment to a fundamental business driver at a pace unprecedented in modern technology adoption.
According to the latest McKinsey Global Survey on AI, 88 percent of organizations now report regular AI use in at least one business function, a substantial increase from 78 percent in 2024.
This acceleration reflects a strategic imperative: organizations are no longer questioning whether to adopt AI, but rather how quickly they can scale it across operations.
Cornell Tech researchers Keith Cowing and Josh Hartmann observe that companies must continue to understand and harness AI’s power, noting that generative AI can perform data analysis and write code, potentially automating complex processes.
The competitive pressure is intense.
The market dynamics underscore this urgency: according to Gartner forecasts, worldwide generative AI spending is expected to total $644 billion in 2025 , representing a 76.4 percent increase from 2024. Organizations are racing not merely to adopt AI, but to fundamentally redesign their workflows and capture competitive advantage before their peers do—a transformation that will separate market leaders from those left behind .
But this rush to adoption exposes the enterprise to a new and complex landscape of AI risks – from costly data breaches and biased model outcomes to severe regulatory penalties.
What Are AI Controls?
At their core, AI controls are the complex set of policies, processes, technical mechanisms, and governance frameworks designed to manage, direct, and monitor AI systems and AI models.
AI controls extend far beyond the perimeter of traditional IT security, addressing a new class of risks that emerge across the entire AI lifecycle – from data ingestion and model training through deployment, monitoring, and eventual retirement.
In their article Securing AI in 2025: A Risk-Based Approach to AI Controls and Governance access controls, data protection, deployment strategies, inference security, continuous monitoring, and governance frameworks . These controls are specifically designed to manage threats unique to artificial intelligence systems, such as model drift, algorithmic bias, and sophisticated adversarial attacks.
IBM defines model drift as the degradation of machine learning model performance due to changes in data or in the relationships between input and output variables, noting that even the most well-trained, unbiased AI model can “drift” from its original parameters and produce unwanted results when deployed if not properly monitored over time.
The National Institute of Standards and Technology has documented the complexity of these AI-specific vulnerabilities. In their publication Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (NIST Special Publication 1270), researchers emphasize that bias manifests not only in AI algorithms and training data, but also in the societal context in which AI systems are used, with human and systemic biases combining to form a particularly pernicious mixture.
This holistic risk landscape demands that organizations implement AI control strategies that integrate technical safeguards, governance processes, and continuous validation – treating AI security as a distinct discipline rather than an extension of conventional IT controls.
Why AI Controls Are Critical for the Enterprise
The urgency for implementing AI controls has been massively accelerated by the rise of generative AI. These complex models, often trained on vast, public datasets, introduce a new class of threats.
Without effective controls, an organization is vulnerable.
Security & Data Risks
Generative AI models can “memorize” and inadvertently leak sensitive data from their training sets. They also create new attack surfaces, such as prompt injection attacks, that can bypass traditional security controls.
Compliance & Legal Risks
Regulators are moving quickly. Frameworks like the NIST AI Risk Management Framework (AI RMF) and the EU AI Act are setting new standards. Non-compliance can lead to massive fines and operational shutdowns.
Ethical & Reputational Risks
If an AI system produces biased, inaccurate, or unethical results, the reputational damage can be catastrophic. AI controls are the mechanism for ensuring fairness, accountability, and ethics.
This is no longer just an IT problem; it’s a core business risk. Building trust with customers, clients, and regulators is paramount, and that trust is built on a foundation of verifiable controls.
The 4 Pillars of a Robust AI Control Framework
A successful strategy isn’t just a random checklist of controls; it’s an integrated framework. A mature approach to AI controls rests on four key pillars that work in concert.
Pillar 1: Governance & Risk Management
Before a single line of code is written, you must establish clear governance. This pillar involves:
Establishing clear lines of responsibility and accountability for AI systems (e.g., the Chief AI Officer).
Formally adopting a risk management framework, such as the NIST AI RMF, to guide all AI-related decisions.
Implementing a process to conduct a formal risk assessment for every new AI use case to identify potential harms and required controls.
Pillar 2: AI Security & Data Protection
This pillar focuses on protecting the AI system itself, its data, and its outputs. This is where AI security and security controls become paramount. Key controls include:
Strictly limiting who can access, modify, or query AI models and their underlying sensitive data.
Ensuring the data used for training is accurate, unbiased, and compliant with privacy regulations .
Implementing controls to protect models from attacks, such as data poisoning, model inversion, and prompt injection. This often involves adopting a Zero Trust security mindset for AI agents and systems.
An AI model is not a “set it and forget it” asset. Its performance can and will degrade over time. This pillar ensures that models remain accurate, reliable, and effective.
Rigorous, documented testing of models before they are deployed to ensure they meet performance and safety benchmarks.
Implementing automated systems to monitor models in production. This is essential for detecting model drift (performance decay), data drift (changes in input data), and unexpected anomalies.
Maintaining detailed, immutable logs of model predictions and operations. This data trail is essential for auditing AI behavior and for post-incident investigations.
Pillar 4: Ethics, Transparency & Accountability
This is the pillar that builds enduring trust. It ensures that AI systems are fair, understandable, and aligned with human values.
Bias & Fairness: Implementing controls to test for and mitigate algorithmic bias across different demographic groups.
Transparency & Explainability: Using techniques (often called Explainable AI, or XAI) to make complex model decisions understandable to human operators and audit teams.
Human-in-the-Loop (HITL): Establishing processes for human review and intervention, especially for high-risk decisions. This ensures final accountability rests with the organization, not the algorithm.
Implementing a Structured Approach to AI Controls
Effective implementation of AI controls is not ad-hoc or reactive; it requires a structured, enterprise-wide approach that weaves governance and security into the organizational fabric from inception to deployment.
Microsoft’s AI strategy framework in the Azure Cloud Adoption Framework emphasizes that a documented AI strategy produces consistent, faster, and auditable outcomes compared to ad-hoc experimentation, requiring structured planning across four core areas: identifying AI use cases that deliver measurable business value, selecting appropriate AI technologies aligned to team skills, establishing scalable data governance, and implementing responsible AI practices that preserve trust and meet regulatory requirements.
This systematic approach is critical because most organizations have not yet matured their AI governance capabilities.
IBM’s guidance on AI governance stresses that effective governance structures in AI are multidisciplinary, involving stakeholders from technology, law, ethics, and business. Organizations need to implement visual dashboards for real-time system health updates, health score metrics for AI models, and extensive oversight mechanisms.
The implementation journey typically follows three critical phases:
1
Conducting Risk Assessments
First, conducting risk assessments to identify AI-specific risks and define organizational risk tolerance;
2
Deploying Technical and Administrative Controls
Second, deploying necessary technical and administrative controls directly integrated into AI development lifecycles (such as MLOps) so security becomes intrinsic rather than retrofitted;
3
Establishing Continuous Monitoring
Third, establish continuous monitoring, regular auditing schedules, and feedback mechanisms to evolve controls as both technology and threat landscapes shift.
This structured implementation requires cross-functional collaboration among data scientists, security professionals, legal experts, and business leaders – making it a complex, multidisciplinary endeavor where many enterprises benefit significantly from partnering with specialized consulting services to navigate the technical and organizational complexities.
AI Controls as Your Business Enabler
AI controls are not barriers to innovation. They are its primary enablers.
They are the framework that provides the safety, security, and compliance necessary for your organization to harness AI’s full potential with confidence.
Managing AI technologies responsibly is the new frontier of data governance. Establishing robust AI controls is the first and most critical step to building enduring trust and sustainable business value.
