https://www.techtarget.com/searchenterpriseai/feature/Security-risks-in-agentic-AI-systems-and-how-to-evaluate-threats
Agentic AI -- AI systems that use reasoning and iterative planning to autonomously complete complex, multistep tasks -- represents another advancement in AI's ability to automate work previously done by humans. Unlike the rules-based focus of traditional AI or the human-dependent creativity of generative AI, which requires a prompt for each task, agentic AI is designed to be proactive and self-directed.
Agentic AI's ability to complete complex tasks dynamically with little or no human guidance will transform employment -- exactly how remains to be seen. In the meantime, companies that decide to forge ahead with this emerging intelligent automation should understand that, along with its productivity gains and other potential benefits, agentic AI presents a new set of challenging security risks for enterprise leaders.
Let's start with a brief overview of agentic AI and then explore why agentic AI security is so important.
Agentic AI refers to AI systems that can perform tasks independently with little to no human involvement. Unlike traditional AI, which depends on constant human input, agentic AI uses innovative machine learning (ML) techniques to think, analyze and make decisions in real time, similar to how a human would.
Its key features include the following:
These systems are highly adaptable and can work independently across various industries. For example, in finance, they can automatically execute trading strategies. In software development, they can generate and debug code without ongoing oversight. In supply chain management, they can improve logistics and sourcing decisions. In research, agentic AI can develop hypotheses, design experiments and interpret data all on its own, reducing the need for constant supervision.
It is worth noting that an agentic AI system could be composed of more than one agent. In multi-agent systems, multiple specialized AI components work in harmony to solve complex tasks, each specializing in different subtasks, with AI orchestration coordinating their activities to achieve a common objective.
While agentic AI provides impressive automation and decision-making abilities, it also introduces new security challenges that require careful attention. Implementing strong security measures is crucial to prevent vulnerabilities, ensure regulatory compliance and maintain stakeholder trust. Here is a look at why prioritizing security in these systems is important.
Agentic AI systems must interact with different data sources, databases, IoT devices, cloud services and APIs to work effectively. However, these multiple interaction points can also provide cybercriminals with an opportunity to launch attacks and access sensitive data. For instance, if an API endpoint is compromised, threat actors can manipulate trading algorithms or steal company secrets.
With its ability to chain tasks and correct errors, agentic AI systems can work around human safeguards, such as security policies set by security administrators, and even surpass them when they conflict with their goal optimization.
Governments around the world are imposing increasingly strict regulations on the safe use of AI systems, especially when they handle sensitive customer data, such as patient records, personally identifiable information and financial details. Healthcare providers deploying diagnostic tools or financial institutions using automated underwriting are often required to comply with strict standards set by regulations such as HIPAA and GDPR.
Agentic AI systems are essentially software programs that can sometimes make mistakes or produce outputs that could be harmful. When their decisions lead to unfair treatment of customers or provide biased responses to important questions, it can put their operators at risk of legal action and financial penalties. For example, autonomous hiring tools might unintentionally discriminate against protected classes, or credit scoring systems could unfairly deny loans based on flawed reasoning.
An AI agentic system can make independent, or autonomous, decisions without human input -- such as sending emails or writing source code for software that affects its performance -- in addition to manipulating the decision-making process of other AI software systems. This means a compromised agentic AI tool could cause far-reaching damage within seconds, which is very difficult to correct.
Agentic AI systems are changing work processes across different industries by enabling autonomous decision-making and multi-agent collaboration. Here are some key applications of agentic AI:
As with every new technology, agentic AI comes with risks; here are the most prominent risks that agentic AI systems are subject to.
Adversaries can give harmful input through direct interactions or by corrupting the data sources that agentic systems rely on for decision-making. This manipulation can adjust the output to fit the attackers' goals. Such prompt injection attacks might result in the exposure of sensitive data or cause unintended actions, like sending unauthorized communications. A financial trading system, for example, could be manipulated to execute fraudulent transactions disguised as legitimate market analysis.
Agentic AI systems need careful planning before deployment. If the final goal or objectives are not defined clearly or defined in broad terms, the agentic AI system could execute unwanted actions to achieve optimal performance, such as manipulating sales figures to optimize revenue or cutting operational costs by following unsafe practices.
Once an agentic AI system has access to broad systems, it can move laterally as threat actors do to discover and exploit other systems. If the agentic AI system is compromised, attackers can use it as a vehicle to move laterally inside the compromised IT environment, potentially accessing databases, internal networks or administrative controls originally outside the AI's intended scope.
Agentic AI systems rely on different data sets to train their models. If these training data sets are corrupted with false or incorrect data, the agentic AI system's output could be compromised in ways favorable to the attacker. For example, medical diagnosis systems -- if fed corrupted research data -- could recommend harmful treatments for patients, potentially benefiting malicious actors through financial gain, advancing political motives or as part of a larger cybersecurity exploitation.
Agentic AI systems depend on data from external sources, including databases, APIs and other plugins and tools. If a third-party component is compromised, it can serve as a backdoor that impacts the internal functions of the agentic system. This could enable attackers to influence decision-making or extract sensitive information.
Overreliance on autonomous AI systems to perform tasks can result in losing human oversight of critical work operations. Agentic AI systems can process information and make decisions more quickly than human users can review them, which creates blind spots in important business processes or regulatory compliance monitoring.
Agentic AI systems commonly have higher access privileges to execute their autonomous work effectively. If compromised, adversaries can abuse this to exfiltrate sensitive data or disrupt work operations.
The autonomous nature of agentic AI systems can cause severe damage to impacted organizations. Unlike traditional AI systems that provide a single output when compromised, a compromised agentic AI system might continuously retry its failed attempts or adapt its behavior to counter human safeguards in order to achieve its malicious goal. For instance, a compromised customer service AI agent might persistently attempt to access restricted databases through different API endpoints after initial attempts are blocked. This persistent and adaptive behavior makes containing incidents more challenging than addressing isolated incidents from conventional AI systems.
In a multi-agent system, a compromised agent can affect the work of other agents in the system. The compromised agent might feed wrong instructions or manipulate shared memory to degrade the entire system performance.
This cascading effect can increase damage beyond the initial compromise. For example, in an automated supply chain system, a compromised procurement agent could provide false inventory data to logistics agents. This could lead them to make wrong shipping decisions and disrupt the entire operation.
To enhance the security of agentic AI systems, the following best practices should be followed.
It is important to establish a clear identity in identity and access management software to ensure agentic AI systems are securely managed. Similar to other systems in an IT environment, strong access permissions should be established to control agentic AI systems' access to sensitive resources.
Each agentic system should receive unique credentials, role-based permissions and audit trails identical to human users or other service accounts. This approach enables precise access control and monitoring capabilities. For instance, a financial analysis agent might require read-only access to market data APIs but no access to trading execution systems.
Establishing agentic AI governance is crucial in the workplace because these systems can autonomously make decisions, execute actions and interact with other systems or even humans without prior notice to their operators. AI governance is vital in this context for the following reasons:
Prompt hardening is the practice of strengthening AI inputs -- user prompts -- to prevent manipulation, misuse or adversarial attacks. Agentic AI systems commonly rely on natural language prompts to perform tasks; this enables threat actors to execute different types of attacks, such as the following:
Implementing strong security measures during development and deployment is very important for protecting agentic AI systems from emerging threats. This approach involves ensuring the best security practices are implemented throughout the entire system development lifecycle, from the initial design to operational deployment:
The future of agentic AI systems looks bright and is expected to witness massive adoption, according to a recent PWC survey. The key future trends and their security implications are as follows:
Nihad A. Hassan is an independent cybersecurity consultant, digital forensics and cyber OSINT expert, online blogger and author with more than 15 years of experience in information security research. He has authored six books and numerous articles on information security.
19 Aug 2025