As digital threats increasingly adopt new technologies and evolve, enterprises face a growing need for more advanced defensive measures. Traditional cybersecurity approaches may have been effective previously, but they struggle to keep up with the sophisticated cyber threats facing today’s enterprises.

The power of artificial intelligence (AI) brings new levels of classification, inference, and prediction. Large language models and generative AI extend these capabilities with seamless and advanced processing, understanding and generation of natural and programming languages. Such advancements in AI/ML are enabling a greater level of speed and precision for complex tasks when appropriately customized.

AI agents built on top of these platforms provide automated efficiencies that reduce human workload and error. Recognizing these benefits, organizations are now turning to AI agents specifically designed and developed for cybersecurity. 

AI agents are adaptive AI systems that continuously learn and evolve. One way AI agents can be developed is to assist in detecting new threats in real-time when for a cybersecurity context. Research shows that AI, ML, and deep learning (DL) methods can significantly improve cyber threat detection. By using these capabilities, organizations aim to better their ability to identify and respond to potential security incidents faster and more accurately.

However, while these agents have the potential to bolster cybersecurity defenses, they are not standalone solutions. It is essential to continuously refine these systems and complement them with human oversight. This combination can help mitigate potential risks and ensure comprehensive security.

The need for adaptive cybersecurity in the AI era

Until about 20 years ago, cyber threats mostly came in the form of viruses or worms designed to cause disruption. These were early forms of malware; and though they were a nuisance, they were generally predictable and could be countered with a basic antivirus solution.

Enterprises no longer enjoy the luxury of that era. Traditional security measures (firewalls, antivirus software, etc.) aren’t sufficient to combat threats. These tools, using signature detection and predefined rules, were able to protect systems from known threats. However, present-day techniques are dynamic and adaptive, able to evade these static defenses. They leverage techniques such as polymorphic malware, which can change its code to avoid detection.

Attacker tactics have also grown in complexity and diversity, especially as attackers themselves leverage AI. Ransomware, phishing, and zero-day exploits are just a few examples of AI cybersecurity risks that leverage AI that organizations are up against.

Organizations that rely solely on traditional security measures may face increased vulnerability to AI-driven threats. It’s possible that these threats could be addressed by AI-based defenses. As such, modern enterprises could benefit from exploring a proactive and adaptive cybersecurity strategy. One component of this approach could be the deployment of advanced AI capabilities such as AI agents, which organizations may want evaluate as part of their broader security measures.

AI agents in cybersecurity

Some types of AI agents use advanced ML algorithms and systems to enhance the detection, prevention, and response to threats, as well as carry out security operations. They can identify patterns and predict potential threats with far greater accuracy and efficiency than human agents alone. These AI agents can evolve in response to new information, making them particularly effective against attacks that continuously change their tactics to bypass conventional defenses.

AI agents may offer a range of advanced features that distinguish them from conventional cybersecurity tools.

• Behavioral analysis: AI agents perform anomaly detection by continuously monitoring network traffic and user behavior to identify deviations from the norm. This allows AI agents to detect insider threats or zero-day vulnerabilities that traditional tools might miss.
• Automated threat hunting: Agents can proactively and continuously search for indicators of compromise across an organization’s systems, often uncovering threats before they can cause significant damage.
• Integration within a broader cybersecurity platform: AI agents can integrate with other cybersecurity systems, such as SIEM or SOAR platforms, to coordinate a unified response to detected threats. Some agents can even handle automatically updating and fine-tuning the configuration of traditional tools. 

Continuous learning and adaptation

AI agents use ML and large language models (LLMs) to process data from sources like network logs and threat intelligence feeds. When a new attack is detected, they’d be able to update their models to learn from the specific tactics used. This would allow them to recognize similar threats in the future and upate their knowledge base. With this capability, AI agents would refine their detection methods and respond effectively to emerging threats.

Some AI agents may employ retrieval-augmented generation (RAG) to supplement their knowledge base so they can retrieve relevant information more effectively for application in real-time defense. By incorporating new data and leveraging RAG, AI agents can keep pace with advancing cyber-attacks.

Machine learning algorithms

AI agents would rely on various ML algorithms to adapt to new threats. Some of them belong to the following types of machine learning:

• Supervised learning: Enables AI agents to recognize specific types of threats by training on labeled datasets that contain examples of known attacks.
• Unsupervised learning: Helps AI agents detect previously unknown threats by identifying anomalies in large sets of unstructured data.
• Reinforcement learning: AI agents learn optimal defense strategies by receiving feedback from their environment—rewarding actions that successfully mitigate threats and penalizing those that fail.
• Transfer learning and domain adaptation:   It would allow AI agents to use existing knowledge and adjust to different data environments. 

This blend of ML techniques ensures that AI agents can dynamically adjust their behavior to counteract both known and unknown threats.

Potential benefits of AI agents for enterprise security

The use of AI agents in cybersecurity is still a relatively new domain. While these systems show great promise in transforming how enterprises detect and respond to threats, many of their benefits remain part of a forward-looking vision rather than fully realized capabilities. 

As the field of AI advances, these benefits are quickly becoming more tangible, positioning AI agents as a vital component of future cybersecurity strategies. With such promise, it’s no surprise that “95% of security professionals anticipate that adopting AI cybersecurity tools will strengthen their security efforts.” The following are potential benefits that AI agents developed for cybersecurity purposes may display once their capabilities are fully realized:

Improved threat detection and response times

AI security agents have the promise to significantly boost threat detection effectiveness. Traditional security systems struggle with the volume and velocity of modern cyber threats. With delayed security responses, significant damage is already done. In the future, AI agents could analyze vast amounts of data in real time to detect suspicious patterns and anomalies before a threat escalates. This prospective capability could help minimize the impact of cyber-attacks and allow enterprises to maintain operations despite persistent threats.

Reduction of false positives

Reducing false positives is another potential advantage. Traditional systems often flag legitimate activities as threats, wasting time and resources, while also leading to alert fatigue. With behavioral analysis and anomaly detection, AI agents might help to reduce false alarms so security teams can focus on actual threats.

Scalability

AI agents may help solutions scale. As organizations grow, their networks and endpoints expand. Manual security measures and procedures become difficult to maintain, and security gaps begin to form. Once AI agents become more mature, they could be deployed across vast networks to monitor sprawling systems without sacrificing performance. 

Challenges and limitations in AI agent deployment

Adversarial attacks

Of course, AI agents will present new challenges. One significant issue is their susceptibility to adversarial attacks, where attackers manipulate input data to deceive the AI into making incorrect decisions. AI models that aren’t regularly updated may fail to recognize emerging threats, leaving the AI and your organization vulnerable. Ensuring the security of the AI system itself is important, as a compromised AI agent could potentially become a vector for future attacks within your organization’s network.

Over reliance on AI

Over reliance on AI is another challenge. AI agents have the potential to be highly effective, but their usage can introduce a false sense of complete security. Neglecting human oversight can lead to missed or misinterpreted threats—especially highly sophisticated ones or those that are simply not represented broadly in the training data. Organizations must balance the use of AI with human expertise. Over-reliance on AI can also lead to complacency, where security practices and protocols may not be as rigorously enforced, increasing an organization’s risk.

AI hallucinations

Because AI agents are LLM-powered, the possibility of AI hallucinations must also be considered. AI hallucinations occur when an LLM generates inaccurate or misleading outputs based on incomplete or ambiguous data. In a cybersecurity context, a hallucination could mean misidentifying benign activities as threats or failing to recognize genuine threats due to erroneous interpretations of data. These inaccuracies can lead to wasted resources addressing false alarms or, worse, missed opportunities to mitigate real attacks.

Cost

The cost and complexity of deploying AI agents also pose challenges. Building and maintaining agentic AI systems require specialized knowledge and ongoing investment in infrastructure and training. The complexity of these systems can make troubleshooting difficult, potentially leading to downtime or security gaps.

Ethical and privacy issues

The use of AI agents in cybersecurity introduces ethical and privacy concerns, particularly around data collection and analysis. AI agents would need access to sensitive information, which can lead to privacy issues if not properly managed. Ensuring compliance with legal and ethical standards is crucial to avoiding unintended consequences.

The future of AI agents in cybersecurity

AI agents are expected to play an increasingly critical role in cybersecurity, driven by advancements in emerging technologies, deeper integration with other tech, and the need for more sophisticated threat management. As cyber threats continue to evolve, it’s anticipated that AI agents will need to incorporate new tools and approaches.

Further advancements in ML algorithms will also strengthen the effectiveness of AI agents. As these algorithms become more sophisticated, AI agents will be better equipped to predict and mitigate new types of attacks. Continuous learning will ensure they remain one step ahead of attackers, identifying even subtle changes in behavior that signal a breach.

Integration with other technologies

The future will also see AI agents integrating more closely with technologies like blockchain, cloud computing, and Internet of Things (IoT). For example, combining AI with blockchain can add to the security and transparency of data transactions, making it harder for cybercriminals to tamper with information. In IoT environments, the industry is seeing the emergence of an intent-driven internet of agents (IIOAs). In this new paradigm, AI agents can monitor IoT devices for unusual patterns, quickly identifying and neutralizing potential threats. This multi-layered approach will enable organizations to build more resilient defenses across all aspects of their infrastructure.

Human-AI collaboration

Looking ahead, human-AI collaboration will be key to the success of AI agents in cybersecurity. While AI can rapidly process and analyze data, human oversight is essential for interpreting results, making strategic decisions, and handling ethical concerns. Humans will play a key role in fine-tuning AI systems to reduce bias and ensure decisions align with organizational values. This collaboration will ensure that AI agents are used not just efficiently, but also ethically and strategically, forming the backbone of a robust cybersecurity defense.

Overcoming barriers

Despite these advancements, challenges such as algorithmic bias, data privacy, and the ongoing threat of adversarial attacks will remain. For example, AI systems that aren’t properly trained can inadvertently favor certain types of data, leading to missed threats or false positives. The complexity and cost of maintaining these systems pose barriers, especially for smaller organizations. Addressing these challenges is vital to ensuring AI agents can be deployed effectively and securely.

AI agents are still in an evolving stage. They can bring improvements in threat detection and response, but they are far from flawless and continue to require refinement to address emerging threats effectively. This ongoing development underscores the need for careful oversight and adaptability as AI agents become more integral to security strategies.

Level up your security with AI agents

As cyber threats grow more sophisticated, the demand for advanced defensive measures has never been greater. AI agents have the potential to become indispensable in modern cybersecurity, leveraging ML to analyze vast data, detect anomalies, and respond to threats in real time. They could provide a proactive, adaptive layer of defense that far surpasses today’s traditional security tools.

AI agents offer many prospective benefits, but they also come with challenges, such as AI security vulnerabilities to adversarial attacks and the risk of over-reliance on automation. The success of agentic AI systems will be most effective when combined with human oversight, ensuring that security practices remain rigorous and AI-driven decisions are ethical and aligned with organizational goals.

As agents become more advanced an Internet of Agents, that will allow for agents to communicate and collaborate across diverse environments using standard protocols, is needed. Learn how Outshift is paving the way.