AI Safety for Field Operations: Best Practices and Protocols

In recent years, artificial intelligence-based solutions for field operations have become widespread. Specifically, this technology can be utilized in tech maintenance, logistics, emergency rescue, and many other sectors. AI solutions include smart drone control, real-time route optimization, equipment diagnostics, and more, to significantly accelerate workflows and improve decision-making accuracy. At the same time, any AI error in the field carries not only financial and reputational risks but also direct physical risks to personnel, equipment, and the environment. Below, we will consider these risks in more detail and share our insights on how to minimize them.

AI Safety: Definition and Importance

AI in field operations is the utilization of machine learning, computer vision, and/or highly intelligent automation to assist live specialists or completely replace them in real conditions, subject to the influence of numerous external factors. In particular, it’s especially useful in predictive maintenance of equipment, automatic inspection of infrastructure, optimization of logistics processes, as well as decision-making support in critical situations – in construction, rescue operations, etc. Ultimately, with the correct implementation of AI in field operations, it increases workflow efficiency, minimizes downtime, and reduces operational costs.

Key Benefits of Using AI in the Field

The implementation of AI brings significant advantages to businesses conducting regular field operations, including:

• Reduction of operating expenses, achievable due to, for example, route optimization and/or prevention of costly accidents;
• Increased accuracy and speed due to the ability to process huge volumes of data from sensors in a matter of moments, and to do it faster and more accurately than a human;
• Improving personnel safety due to the ability to delegate dangerous tasks such as inspection in confined spaces or at height to AI-controlled devices;
• An increase in the service life of assets, achieved through highly accurate predictive maintenance (instead of a reactive one).

In field operations, when the AI algorithm can give an incorrect answer when assessing the strength of structures or any other objects that pose a threat, it becomes clear that its safety is not just about compliance with standards, but also about the safety of the lives of human specialists.

Common AI Risks in Field Operations

The risks associated with AI in the field can be divided into three main groups.

Technical risks

These are risks associated with the shortcomings of the software itself or the hardware on which it works. In particular, we are talking about algorithmic errors (when the AI model encounters data that differ from those on which it was trained) as well as malfunctions in the operation of AI-powered devices – drones, cameras, sensors, wearable equipment, etc. – in the event of their breakdown, GPS failure, or battery discharge, the operator may lose control or receive false data.

Operational risks

These risks include human-AI interaction mistakes, when personnel "blindly" trust AI recommendations, ignoring their own experience and common sense, or, on the contrary, do not trust, turning off the system when it is needed. This group also includes miscommunication in field teams, when information generated by AI is transmitted incorrectly or untimely, which ultimately risks leading to incorrect actions or violations of security protocols.

Security and ethical risks

These risks include data privacy concerns when the AI collected sensitive data, and it was lost or fell into the hands of unauthorized third parties. Also, this category includes dilemmas on ethics in AI decisions, when the AI is faced with a dilemma such as "continue the inspection or return due to a weak charge to avoid falling into private territory."

AI Risk Management Strategies

Successful AI risk management in field operations is based on a multi-layered approach that includes ensuring:

• Robustness and redundancy. This implies, first of all, the introduction of fail-safe mechanisms and redundant control systems (for example, the use of two sensors to measure the same parameter or the creation of a backup communication channel).
• Real-world validation. Models must be tested under the influence of various external factors (weather conditions, lighting, pollution, etc.), and not only in laboratory conditions. Here, synthetic data and emulations will come in handy to help you prepare the model for critical scenarios.
• Human-in-the-loop approach. This approach implies the establishment of decision points where the operator must either confirm or cancel the AI recommendation to preserve control and responsibility for a human and not a machine algorithm.
• Data security protocols. In this context, you must use data encryption at rest (while it’s on the device) and in transfer (i.e., when this data is transferred from device to device), and also implement access control and anonymization of personal data before using it in training models.
• Governance and documentation. Finally, you’ll need to develop detailed working instructions and AI decision-making rules, so that in the case of an incident, you can determine exactly why the system made this or that decision.

Of course, this is only a basic description of the approaches that will enable you to introduce AI safety in field operations – in the end, a clear list of what needs to be implemented will depend on your field of activity and the tasks for which you intend to use AI.

Identifying and Prioritizing Risks

Risk management is a continuous process consisting of their systematic identification and assessment.

Risk assessment tools for field operations

The following tools will help you assess risks:

• Probability/impact matrix – a basic tool, where each identified risk is assessed by the probability of its occurrence (from rare to very frequent) and its impact on business and security (from low to catastrophic);
• Root cause analysis – it’s an analysis conducted after any incident or system failure, and determines the true cause of the error;
• Failure mode and effects analysis – a method that analyzes each component of the AI system (both software and hardware) and determines failure modes and what follows.

Together, these tools will help you prioritize risks, understand their causes, and determine the severity of their consequences.

Mitigation techniques

After identifying the risks, the first thing to do is ensure the system redundancy (this applies to critical functions – they require support by redundant systems). Also, you should limit the working area by prohibiting the AI system from operating beyond clearly defined, safe parameters. Finally, you must implement fail-safe mechanisms that, upon detection of an anomaly or low model reliability, must immediately hand over control to a live specialist and switch to a predetermined, safe behavior.

Continuous monitoring and feedback loops

Since the external environment is constantly changing, AI systems require continuous monitoring after deployment – that’s why it’s crucial to implement feedback loops to ensure that data about failures and errors that have occurred in the field will automatically be sent to the development team to retrain the model and update security protocols. This allows the AI system to gradually adapt to data drift (which, in turn, implies a change in the characteristics of the input data).

AI Safety Protocols for Field Operations

The development of AI safety protocols requires a clearly defined scenario for critical situations. These include halt and rollback protocols (in essence, these are instructions about who, how, and under what conditions can immediately disable the AI system and roll it back to the verified version), contingency plans (step-by-step instructions for operators in case of communication failures, incorrect AI decisions, or external negative factors), as well as verification procedures before deployment (each new version of the AI model must undergo mandatory two-stage testing first through a simulator and then in a controlled field environment).

As for compliance with industry and international safety standards, we can highlight the following ones:

• ISO 26262 – although intended for the automotive industry, this standard is often used to assess the functional safety of AI systems as well;
• ISO 27001 and other cloud standards – they are aimed at ensuring the security of data collected and processed by AI solutions;
• Regional regulatory requirements – here, we mean local regulations for the use of drones, requirements for labor protection and technical safety, etc. 

Finally, to maintain a high level of AI security, you’ll need to conduct regular security audits with the participation of independent teams that would test the code, the quality of training data, and the security protocols of AI systems themselves. These audits should be supplemented by decision logging (this includes a description of AI decisions, input data, the level of confidence of the model, and the final action), as well as maintaining a reporting system within teams using a specific AI decision.

Human Factors in AI Safety

Safety depends largely on effective human-machine interaction – that's why your personnel must clearly understand what an AI system can and cannot do. This implies training your employees on what failure indicators look like and how to perform an immediate handover. You'll also need to ensure bias awareness by clearly explaining how data bias can influence AI decisions.

Another excellent practice is to define clear lines of responsibility (i.e., which decisions the AI is responsible for, and which the operator is responsible for) and ensure that the user interface of your AI solution is simple and reflects the AI's confidence in its decisions.

Finally, you have to always start with yourself: if you demonstrate a commitment to AI safety to your employees, allocate the necessary resources, and encourage reporting of potential risks, you’ll reap the rewards of rapid and effective implementation of even the most sophisticated system.

Technologies Supporting AI Safety in Field Ops

Effective implementation of AI safety protocols is impossible without the use of specialized technologies, which will be discussed below.

Safety monitoring software

This type of software monitors in real time both the state of the AI system and the physical environment in which it operates. Specifically, solutions like Datadog and Grafana can track resource utilization (including GPU/CPU load, device temperature, battery charge, etc.), the level of confidence in AI predictions, and sensor failures (which are necessary to activate fail-safe mechanisms).

Predictive analytics for risk prevention

Predictive analytics uses historical data on failures, incidents, and operational parameters to identify the likelihood of a risk occurring. For example, solutions like Siemens MindSphere and GE Predix can predict the number of hours until a failure occurs or determine the environmental conditions under which an AI solution will be ineffective (i.e., its accuracy will be low).

AI simulation and testing environments 

Because testing in real field conditions often involves danger, virtual simulators like NVIDIA Isaac Sim and Omniverse can help you test models against millions of scenarios in realistic environments and train operators for emergency response using digital twins of field systems.

Examples of Successful AI Safety Implementations in Field Operations

Generally speaking, when safety protocols are followed, AI can significantly optimize operational reliability in the following use cases:

• AI-enabled predictive maintenance for the energy sector. Many modern energy companies are implementing AI to analyze data from wind turbine sensors. Moreover, some of these AI-powered solutions can, in addition to predicting failures, provide comprehensive data on the probability of predictions. Specifically, if AI confidence is low, such solutions autonomously initiate remote video inspections, involving human experts (instead of automatically canceling turbine operations and thereby preventing costly downtime when it’s unnecessary).
• Ensuring safety on construction sites through computer vision. AI cameras are often used on construction sites to detect unsafe behavior by construction workers. For example, if the AI detects that an employee doesn’t wear a hard hat, it will automatically send a warning to the supervisor. Such systems are also trained to ignore false alarms (for example, when this hard hat is in the hand) to prevent excessive oversight and increased workload on the operator.
• Conducting remote inspections of pipelines. Today, there are AI-based robot inspectors that move along pipelines according to fail-safe protocols: in case of loss of communication or detection of critical damage, they independently stop, record their location, and activate an emergency beacon so that live specialists can evacuate them.

If you would like to implement one of the aforementioned solutions in your business or have another idea to boost your regular field operations through AI, feel free to contact us.

FAQ