Types of AI Controls

Artificial intelligence is not just a buzzword anymore; it is everywhere, from chatbots resolving customer queries to algorithms driving cars. In fact, 78% of organizations now use AI in at least one business function, up from 55% just a year earlier. This exponential growth comes with a twist: along with enthusiasm, the share of business leaders who view AI as a potential risk has more than doubled (from 5% to 11% in one year). Staying ahead in cybersecurity means understanding how to harness AI’s power safely and responsibly. That’s where AI controls come in. AI controls are the rules, mechanisms, and safeguards that ensure AI systems behave as intended, keeping them on track, ethical, and secure.

Understanding AI Controls and Why They Matter

An AI control is any measure that helps govern or guide AI behavior. This can range from technical (e.g., algorithms that self-correct an AI’s course) to procedural (e.g., company policies or laws about AI use). The goal is the same: to ensure AI systems do what we want them to do, nothing more, nothing less. With AI making more decisions on our behalf, having the right controls in place is as critical as having good brakes in a fast car.

Types of AI Controls 

Below are the different types of AI controls:

1. Technical Control

One fundamental type of control comes from the field of control theory in AI, which is all about embedding feedback loops and stability mechanisms into AI systems. It is designing intelligent systems with the ability to monitor and adjust their own actions to achieve desired outcomes. For example, autonomous drones use control theory principles to constantly adjust their flight path and speed based on sensor feedback, ensuring they stay stable and avoid obstacles in real time. These technical controls (like PID controllers or adaptive algorithms) act as an AI’s internal compass and steering system, keeping its behavior within safe and optimal bounds.

2. Human Oversight and Governance Controls

No matter how “smart” AI gets, keeping humans in the loop is often the ultimate safety net. Human oversight controls ensure that there is always a layer of human judgment either supervising the AI’s actions or ready to intervene if things go sideways. This can be as simple as requiring human approval for certain AI decisions (say, an AI flagging a financial transaction for fraud), or as dramatic as an emergency “kill switch” that immediately shuts down an AI system in crisis. Frameworks like the Cloud Security Alliance’s AI Controls Matrix (AICM) also embed governance requirements. The AICM spans 18 security domains and 243 control objectives; from traditional areas like Identity & Access Management and Data Security to AI-specific needs like model security, transparency, and accountability.

3. Preventive Controls in AI Design

One of the smartest ways to control AI risks is to prevent problems before they start. Preventive AI controls are implemented during the design and development phase; these are like “baking in” safety and ethics into the AI. These are often called design-time controls because they happen before deployment. Examples of preventive controls include:

• Data Controls: Carefully curating and vetting training data to avoid biases or errors propagating into the model. For example, removing sensitive personal data or ensuring diverse representation in the dataset helps prevent biased outcomes from day one.
• Robust Model Design: Using techniques like adversarial training (exposing the model to potential attacks/noisy data during training) to make it more resilient. Similarly, fairness-aware algorithms can be chosen to mitigate discrimination in AI decisions from the start.
• Validation and Stress Testing: Before an AI ever sees real users, developers perform extensive testing, simulating edge cases and “worst-case scenarios” to see how the AI holds up. For example, an image recognition AI might be tested on altered lighting or occluded images to ensure it still performs well. If weaknesses are found, tweaks are made early.

4. Detective Controls: Monitoring AI

Even with great design, once an AI system is live, you need to keep an eye on it. Detective AI controls are all about monitoring and detection; spotting when something might be going wrong (or about to). These are often run-time controls, meaning they operate while the AI system is in use. Key detective controls include:

• Performance Monitoring: Tracking the AI’s outputs and accuracy in real time. If a customer service chatbot’s helpful answer rate suddenly drops, that dip gets flagged. Continuous metrics and dashboards can reveal if the AI is straying from expected behavior.
• Drift Detection: Over time, an AI model might become less effective if the world changes (e.g., consumer preferences shift, new types of data emerge). Automated drift detection systems watch the input data and model outputs for statistical shifts. If the incoming data starts to look very different from the training data or error rates creep up, the system raises an alert.
• Anomaly and Threat Detection: In a security context, we deploy tools to catch unusual behavior, whether it is an AI system being attacked or simply making a bizarre decision. For example, if an AI that controls network traffic suddenly begins sending data to an unknown server, an anomaly detection control would notice and report it. Similarly, adversarial attack detection can be in place to spot if someone is feeding malicious inputs to trick the AI.

5. Response Controls: Intervention and Correction

If preventive measures are the seatbelts and detective measures are the warning lights, response controls are the emergency brakes and airbags. Some important response controls are:

• Automated Shutdown or Rollback: When an AI system goes off the rails (for example, a trading algorithm making erratic trades), automated controls can disable the AI or revert to a last known good state. This might mean rolling back to a previous model version that was stable.
• Human Override Procedures: Many AI deployments include a manual override, a way for a human operator to immediately take control or shut down the AI. Imagine a self-driving car handing control back to the human driver under certain conditions, or a big red “stop” button on an industrial AI system. These kill switches or overrides are classic response controls that ensure ultimate authority remains with humans.
• Incident Response Protocols: Organizations should treat AI incidents like cybersecurity incidents, with predefined playbooks. If an AI system causes an error or fails a compliance check, a response protocol might involve alerting a response team, diagnosing the issue, and applying a fix or patch.

6. Regulatory and Ethical Controls

Finally, beyond the technical and operational controls, there’s a bigger picture: the regulatory, ethical, and compliance framework that acts as an external control on AI. Governments and industry bodies are increasingly stepping in to set boundaries on AI; essentially saying, “here’s how you are allowed to use it.” For example, forthcoming regulations like the EU AI Act will classify AI systems by risk and impose requirements (or even prohibitions) on certain high-risk use cases.

Advanced in AI Audit (AAIA) Training with InfosecTrain

Artificial intelligence may be transformative, but it is not set-and-forget magic. Just as you would not drive a sports car without safety features, we should not deploy powerful AI without robust controls. We have explored the types of AI controls, from technical control theory mechanisms ensuring stability, to human-in-the-loop oversight, preventive design safeguards, vigilant monitoring, rapid response measures, and the guiding hand of governance and ethics. Each type addresses a piece of the puzzle in managing AI risk and performance.

That’s exactly what InfosecTrain’s Advanced in AI Audit (AAIA) Training prepares you for: mastering preventive, detective, and response controls while aligning with global AI risk standards.

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