There is a boom in AI agents. They can spin up, act on their own, use tools, and make decisions, even without real-time human intervening. They promise improved productivity but also expose themselves to risks and challenges that cannot be overlooked. Since these agents become more autonomous and are adopted within the organizational operations, they bridge the gap between human and machine responsibilities. This raises a major concern of how to ensure ethical acting and secure their identities and access. Similar to human beings, ensuring AI agents work safely and ethically throughout their lifespan is important for the business, employees, customers, and reputation. In this article, we will explore the life of an AI agent in comparison to a human’s life from birth to death. Finally, it will explore the identity security lessons from this comparison. Let’s begin with understanding what an AI agent is.
What is an AI Agent?
AI agents are autonomous software entities running on large language models. They have the ability to reasoning, make decisions, and use tools. They can be integrated into SaaS apps, executed from a browser, or run across specific agentic platforms or deployed as white label voice AI agents that operate under an organization’s brand and identity in customer-facing workflows.
Secure Beginnings for AI Agents
There is a specific birthplace for every agent- a cloud platform, a specialised agentic framework, a SaaS platform, or even a local browser. However, regardless of the place, the environment should be tightly controlled and secured.
In neonatal intensive care, newborns are protected with tighter hygiene practices, continuous vigilance, controlled access, and continuous recording. Similarly, AI agents should be born safely into trusted, robust environments with strong identity, authentication, access controls, and monitoring from the start. As the safety of a baby depends on vigilant care and sterile tools, the trustworthiness of AI agents begins with a clean, well-managed foundation. A little mismanagement of AI agent security can entirely disrupt or harmfully impact the activities of the AI agents.
Learning from Reliable Data
As kids go to school and learn the survival skills, knowledge, values, and social norms that shape their attitudes and future contributions, AI agents should gain knowledge and lessons that determine how they understand and engage with the world. After birth, agents must learn, and the lessons learned should be important. Their reasoning and behaviour are influenced by the large language models and refining data. However, manipulated or biased data can result in injecting bad actors into agents with flawed decision-making, fabricated assumptions, or exploitable reasoning.
This is how AI agents engage with the tools. They increasingly depend on APIs and functional calls. Lack of proper guidance and preventive measures can result in unintended actions like data deletion, automation loops, or exposing private information.
To ensure the security of this education phase, organizations must ensure:
- Verified, reliable training data set
- Set limitations for agents on which tools they should use
- Test and simulate AI agent activities before launching it
Ineffective training can pose a potential risk for the agent. Organizations cannot bear the costs of thousands of unpredictable agents acting in the surrounding environment.
Safe Communication within Community
AI agents do not work in a vacuum. They connect with the public, services, and other AI tools. Like humans do not quickly trust each other, depend on passports, ID cards, digital authentication approaches, and real face-to-face interaction to establish relationships, AI agents also need clear, verifiable ways to get themselves verified and authorized.
This requirement for verifiable identity leaves room for a complicated web of authentication and authorization needs. However, a lack of proper control can risk agents mimicking another person or an agent, or falling prey to risk or disclosing data in unauthorized ways.
To ensure safe communication within the community:
- Mutual authentication is important through certificates, tokens, and secure APIs
- Agent-to-agent conversations should be managed and logged, mainly when the interactions are between two different processes or platforms.
- Delegation and consent approaches help in determining what agents can do for others
Access controls for AI agents since AI agents represent human beings. It is important to identify the person behind the scenes and not only the agent process.
Taking Responsibilities
Employees are often expected to add value to the company, obey the rules, and act responsibly. Like employers create an official identity for employees at the workplace by providing authentication, defining roles, limiting access, and clarifying responsibilities, organizations should follow the same for AI agents. For security and compliant, employee actions are monitored as they access systems and company data. Similarly, AI agents are also obligated to the same level of accountability. Therefore, identities are important for AI agents to operate safely within an organization. These identity security lessons help to:
Use refined access controls for AI agents
Monitor actions of AI agents through audit trails
Implement compliance and regulatory policies around actions of AI agents.
In the security or operational cases, it is important to monitor the actions of the agents. As human behaviour, AI agent behaviour remains unpredictable. This makes accountability foundational to establishing trust and safely scaling to more ambitious agent-driven measures that could contribute more to the organization.
Promotions of AI Agents to Leadership
As employees who show loyalty over time and are promoted to leadership roles with more responsibilities, like budget management, contract approval, or managing sensitive information, AI agents are also granted additional responsibilities as they start proving their capabilities. However, the increased trust often causes increased risk. Hence, there is a need for stronger vigilance, stringent access controls, and protection to avoid abuse or error.
Some agents will be developed to operate autonomously. Others will integrate with different systems, agents, and humans across workflows. These equipped agents will act as virtual managers, making business-specific decisions.
Demise of the AI Agent
As employees retire or leave by choice, their access, credentials, and organizational identities are also deactivated. Temporary or contractual employees must be given access during their period. The same should be applied to AI agents. This ensures that when AI agents are not working, their identities, access, and relevant credentials are unavailable for use or removed entirely.
Inactive agents that have access even after their suspension turn into zombie agents. There remains a great scope for exposed privileges. This lingering access shows a significant attack surface that attackers can abuse. These identity security lessons can be applied to the AI agents to prevent them from becoming zombie agents.
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