The rapid evolution of cyber threats and the increasing complexity of digital ecosystems have underscored the need for advanced cybersecurity measures. Intelligent agents will emerge as a promising approach for addressing these challenges, leveraging AI and machine learning techniques to detect and mitigate cyber threats in real-time. However, the design and deployment of effective agent-based solutions require a structured approach to ensure scalability, interoperability, and adaptability across diverse environments.

This article provides a reference architecture, tailored specifically for AI cybersecurity agents, providing a refrence architect to guide: testing, design, implementation, and evaluation.

Architectural Reference layers:

This article will introduce a four-layer reference architecture:

• social-technological environmental layer;

• environmental layer;

• agent architecture,; and

• agent function.

Social-technological Environment

The social-technical environment in which AI cybersecurity agents operate encompasses both social and technical elements. It includes interactions with human stakeholders, organisational structures, cultural norms, as well as the underlying technological infrastructure such as communication networks, databases, and software systems. Understanding this environment is crucial for designing agents that can effectively navigate human-computer interactions, address ethical and regulatory considerations, and align with organisational objectives.

Environment Class

The environmental Class based on key characteristics such as determinism, observability, discreteness, and competitiveness is essential for understanding the challenges and opportunities faced by AI cybersecurity agents. Different environment classes, ranging from deterministic to stochastic, episodic to sequential, and fully observable to partially observable, present unique complexities that influence agent behaviour and decision-making strategies. The environment class provide the operating context for the agent(s) (worker agent, decision agent, management, reporting or tasking agent) and establishes the agents objective, goals and activities.

Agent Architecture

The agent architecture for a cyber agent defines their underlying structure, components, and relationships, shaping their ability to perceive, reason, and act in complex environments. Various architectural paradigms, including reactive, deliberative, hybrid, and learning-based approaches, offer distinct advantages and trade-offs depending on the specific requirements of the cybersecurity task at hand.

Agent Function

The function of AI cybersecurity agents encompasses the mapping from percept sequences to actions, driven by the agent's goals, environment, and available resources. This function is shaped by percept sequence analysis, state representation, decision-making algorithms, learning mechanisms, and performance evaluation criteria, enabling agents to adapt and optimise their behaviour over time.

Component elements:

The following section of this article will expand upon each component elements within each architectural layer.

Performance Standard

A performance standard is a set of criteria or benchmarks used to evaluate the effectiveness or success of an AI agent in achieving its goals. These standards define what constitutes satisfactory performance for the agent and provide a basis for assessing its performance over time. Performance standards can vary depending on the specific task or domain in which the agent operates. They may include measures such as accuracy, speed, resource efficiency, safety, or any other relevant factors that reflect the desired outcomes of the agent's actions. Performance standards serve as a reference point for assessing the agent's performance, guiding its behaviour, and informing decision-making processes. 

Agent Architecture

Sensor

A sensor is a device or mechanism that detects and responds to some type of input from the environment. In the context of AI agents, sensors are used to gather data or perceptual information about the surrounding environment. These inputs could be anything from temperature readings, images, sound waves, or any other form of data that the agent needs to understand its environment. Sensors serve as the agent's interface with the external world, providing it with the necessary information to make decisions and take actions.

 Actuator

An actuator is a component or mechanism responsible for producing physical actions or responses based on the decisions made by the AI agent. Actuators enable the agent to interact with its environment by manipulating objects, moving itself, or triggering other events. Examples of actuators include motors, robotic limbs, speakers, displays, or any other device capable of effecting change in the agent's surroundings. Actuators allow the agent to execute its chosen actions and influence the environment based on its goals and objectives.

Agent Function

Percept Sequence

In the context of AI agents, a percept sequence refers to the series of observations or inputs received by the agent from its environment. This sequence forms the basis of the agent's understanding of the world around it. Percept sequences can be simple sensor readings in a physical environment or streams of data in a digital environment.

 State

The state of an agent represents its internal representation or snapshot of the environment at a given point in time, based on the percept sequence it has received so far. It encapsulates all the relevant information necessary for decision-making and action. States can be discrete or continuous, depending on the nature of the environment and the agent's perception.

 Agent Program

The agent program determines the mapping from percept sequences to actions. It defines the behaviour of the agent based on its current state and the incoming percept. This program can be rule-based, where specific conditions trigger predefined actions, or it can be learned through machine learning algorithms, evolving over time based on experience.

 Performance Measures

Performance measures define the criteria for evaluating the success or effectiveness of an agent's behaviour. These measures can include factors such as accuracy, efficiency, robustness, and resource utilization. Performance measures provide feedback to the agent, guiding its learning and adaptation process towards achieving its goals.

 Problem Generator

The problem generator is responsible for identifying and formulating new challenges or tasks for the agent to solve. It explores the environment for opportunities to improve performance or acquire new knowledge. The problem generator can range from simple task generation algorithms to more sophisticated mechanisms that consider the agent's current capabilities and objectives.

Critic

The critic component provides feedback to the agent on its actions and decisions, helping it understand the consequences of its behaviour. It evaluates the agent's performance against predefined goals or criteria and identifies areas for improvement. The critic can be implemented as a separate module within the agent or integrated into the learning process to guide the exploration-exploitation trade-off.

 Learning

Learning mechanisms enable the agent to improve its performance over time through experience. This can involve various techniques such as supervised learning, reinforcement learning, or unsupervised learning. Learning allows the agent to adapt to changing environments, discover patterns in data, and refine its decision-making processes.

 Behaviour

The behaviour of the agent refers to the actions it takes in response to incoming percepts and its current state. This behaviour is determined by the agent program and can be influenced by learning mechanisms, performance measures, and external stimuli. The goal of the agent's behaviour is to maximize its performance or utility in the given environment.

Knowledge

Knowledge encompasses the information, representations, and models that the agent acquires and uses to understand its environment, make decisions, and solve problems. This knowledge can be explicit or implicit, structured or unstructured, and can include facts, rules, heuristics, or learned patterns. Effective knowledge representation and management are essential for the agent to achieve its goals efficiently and adapt to new situations.

Understanding 

Each of these component elements plays a crucial role in defining the function and behaviour of your cyber AI agent, allowing it to perceive, reason, learn, understand, decide and act effectively in its environment.

Conclusion

In conclusion, the development of a reference architecture for AI cybersecurity agents is essential for addressing the complex challenges of modern cybersecurity. By providing a standardized framework, a reference architecture facilitates the design, implementation, and evaluation of robust, scalable, and interoperable agent-based solutions. It enhances the understanding, collaboration, and coordination among agents and human counterparts, ultimately strengthening cybersecurity defences and safeguarding digital assets in an ever-evolving threat landscape.