AI Agents And Their Intersection with Non-Human Identities

Introduction

Artificial intelligence (AI) has become an essential part of today’s digital environments, driving advancements in automation, decision-making, and operational efficiency across various industries. One particularly fascinating development is the intersection of Non-Human Identities (NHIs), digital entities used by AI Agents that take on roles traditionally handled by humans. While these AI-driven NHIs unlock remarkable opportunities for innovation, they also introduce distinct security risks and challenges.

In this report, we’ll break down the technical mechanisms of AI agents within NHIs and explore the cybersecurity threats they pose. We’ll also discuss strategies for mitigating these risks, as NHIs continue to become a more integral part of everyday systems.

Understanding Non-Human Identities (NHIs)

What are NHIs?

Non-Human Identities (NHIs) are digital entities or software-based actors that operate autonomously or semi-autonomously within a system. They perform tasks, make decisions, and interact with other digital or human entities. Unlike traditional software, NHIs are typically used by AI agents, enabling them to display behaviour that might seem intelligent or human-like, even though they lack consciousness.

NHIs can be used in various forms, including:

• Autonomous Agents: These are AI-driven entities capable of perceiving their surroundings, analysing data, and making decisions on their own. For example, a financial AI algorithm that independently conducts stock trades based on market trends is an example of an autonomous agent.

• Digital Twins: These virtual replicas that mirror the behaviour of physical objects, processes, or systems. AI-powered digital twins are used in industries like manufacturing, healthcare, and urban planning to model, analyse, and predict the behaviour of real-world counterparts

• Synthetic Personas: AI-generated personas interact with humans on platforms like social media, customer service, or e-commerce. They simulate human behaviour using natural language processing (NLP) and machine learning technologies.

• AI-Driven IoT Devices: Internet of Things (IoT) devices, such as drones, robots, or smart home systems, are part of the NHI ecosystem. They operate autonomously and communicate with other entities by leveraging real-time data.

AI Agents using NHIs

AI agents are entities that operate in an environment and perform actions autonomously to achieve predefined goals. AI agents may use various forms of machine learning (ML) and deep learning (DL) techniques to improve their decision-making capabilities over time. Their functioning can be categorized as follows:

1 - Perception: The AI agent collects data from the environment via sensors, data streams, or external APIs. For example, an AI agent managing an autonomous vehicle processes real-time sensor data (e.g., from LIDAR or cameras) to understand the driving environment.

2 - Decision-Making: After perceiving the environment, the AI agent processes the data using decision-making models (e.g., neural networks, reinforcement learning, etc.). The agent determines the best action by predicting outcomes based on historical data and predefined reward functions.

3 - Action: Finally, the AI agent executes the action by sending control commands to devices, initiating communication with other agents, or updating system states. In autonomous trading, the AI agent could initiate a buy/sell order based on its evaluation of the market.

Threats Posed by AI Agents using NHIs

AI agents’ autonomy and decision-making capabilities, while beneficial, introduce numerous threats to the integrity, privacy, and security of the systems they operate within.

1 - Unauthorized Identity Access and Manipulation - AI agents using NHIs often have access to sensitive data and critical system controls. If these agents are compromised, attackers can gain unauthorized control over the NHI, allowing them to manipulate decisions or impersonate the entity in malicious ways. Such attacks can involve:

• Hijacking of Control Systems: Attackers may exploit vulnerabilities to hijack AI agents controlling industrial machines, leading to operational disruptions or safety risks.

• Identity Spoofing: Attackers can impersonate an AI-driven NHI, such as a digital persona or chatbot, to deceive users or steal credentials in social engineering attacks.

2 - Adversarial Attacks on AI Models - One of the most deceptive and harmful types of attacks targeting AI agents is the adversarial attack. In this scenario, attackers create malicious inputs specifically designed to confuse the AI model. Compromising NHIs, adversarial attacker can manipulate AI agents into making flawed or incorrect decisions, such as:

• Data Poisoning: Introducing malicious or tampered data into the training process of AI models, corrupting the agent’s learning and future behaviour.

• Model Evasion: Crafting inputs that cause AI agents to misclassify data or fail at their tasks. For instance, in autonomous driving, an adversarial attack might trick the AI into misinterpreting a stop sign, causing a vehicle to ignore traffic rules.

3 - Data Privacy and Security Risks - AI agents using NHIs often process personal or sensitive data, which makes them prime targets for data breaches. Threat actors may exploit vulnerabilities in the system to access confidential information, including financial records, medical data, or proprietary algorithms.

• Inference Attacks: Attackers can analyse the output of AI agents to infer private details about individuals or entities, even when direct access to the data is restricted.

• Data Exfiltration: AI-driven NHIs are also at risk of data leakage, where attackers infiltrate systems and steal sensitive information without detection.

4 - AI Autonomy Risks - The autonomy granted to AI agents, while improving efficiency, creates risks when AI-driven NHIs make unintended or harmful decisions due to incomplete data, unforeseen conditions, or model flaws. Examples include:

• Autonomous Vehicles: AI agents controlling self-driving cars could misinterpret their environment due to unexpected stimuli or sensor errors, leading to dangerous outcomes.

• AI in Healthcare: Medical NHIs could misdiagnose conditions or recommend inappropriate treatments due to flawed AI models.

Mitigation Strategies for AI Threats and NHIs

To secure NHIs from these threats, a range of technical and policy-driven mitigation strategies must be adopted.

1 - Secure AI Model Development

• Adversarial Training: AI models should be trained using adversarial examples to enhance their robustness against malicious inputs. Techniques like robust optimization and randomization can also strengthen AI agents against adversarial attacks.

• Regular Model Audits: Continuous testing and auditing of AI models can help identify and rectify vulnerabilities in AI decision-making. Security teams should run periodic evaluations to check for bias, unintended behaviours, and security flaws.

2 - Implementation of Strong Access Control

• Multi-Factor Authentication (MFA): To prevent unauthorized access, AI agents and NHIs should be secured with strong authentication mechanisms, including MFA. This is critical in preventing attackers from gaining control of sensitive NHIs.

• Role-Based Access Control (RBAC): Only authorized users or agents should have access to critical AI functions or sensitive data. Implementing RBAC ensures that access is restricted to trusted parties based on the principle of least privilege.

3 - Zero Standing Privileges

• Transition to a Zero-Trust model by moving away from static secrets to Just-In-Time (JIT) ephemeral secrets making it much hard for a threat actor to compromise NHIs.

4 - Data Privacy Compliance

• Data Encryption: Sensitive data processed by AI agents must be encrypted both in transit and at rest. This ensures that even if an attacker gains access to the data, it remains unusable without the decryption keys.

• Differential Privacy: AI models should be designed with differential privacy, ensuring that individual data points cannot be reverse engineered from the model’s outputs.

5 - Continuous Monitoring and Incident Response

• Anomaly Detection Systems: Deploying anomaly detection systems powered by machine learning can help identify deviations in AI agent behaviour, indicating any potential security incidents or adversarial attacks.

• AI Forensics: In the event of an attack or system failure, AI forensics should be used to understand the cause and assess the impact of the incident. Forensic tools must be tailored to trace AI decision-making processes.

Conclusion

As AI agents become the driving force behind using Non-Human Identities (NHIs), it is essential to recognize the unique threats they pose to security and privacy. From adversarial attacks to ethical concerns, the risks associated with AI-powered NHIs can be substantial if not properly managed.

However, by adopting robust AI model development practices, enforcing strict access controls, a zero trust model using ephemeral secrets and ensuring data privacy and compliance, organizations can mitigate these threats and unlock the full potential of NHIs in a secure and ethical manner.

The future of AI-driven NHIs will undoubtedly transform industries and redefine the digital ecosystem, but addressing their inherent security challenges is critical to their long-term success.