Security teams across the world are battling severe crises, including personnel shortage and burnout. In addition, the attack surface gets bigger and more complex as new technologies like Artificial Intelligence (AI) and adoption of Software-as-a-Service (SaaS) increases. Wouldn’t it be great if the process, or parts of it, could be automated to give some relief to the analysts? Agentic AI allows you to do just that, i.e. autonomous cyber defense. Agentic AI cybersecurity can function autonomously, reducing the load on the SOC personnel. It can take care of one of the biggest headaches: alerts. Agentic AI allows automation of the alert and triaging process. Its agenting nature will also allow various security functions to be handed off to multiple agents. Thus, one agent can handle the task of scanning alerts and enriching them by adding contextual data, such as user behavior and system configurations. Similarly, agents can handle tasks like vulnerability scanning. The automation means reduced pressure on the SOC, allowing them to work under (somewhat) less pressure. Here is a table describing the various advantages and disadvantages of adoption of Agentic AI.
AI vs. Agentic AI in Cybersecurity
| Aspect | Traditional AI | Agentic AI |
| Nature | Pattern recognition, classification, and prediction based on data. | Goal-driven, autonomous decision-making and execution. |
| Role in Security | Supports analysts by flagging anomalies or threats. | Acts as an autonomous agent by detecting, deciding, and responding without (or with minimal) human input. |
| Scope | Narrow, task-specific (e.g., anomaly detection, phishing email classification). | Broad, multi-step reasoning across interconnected tasks (e.g., detect, contain, and remediate incidents). |
| Autonomy | Dependent on human oversight for action. | Operates independently, can take proactive/defensive actions in real time. |
| Adaptability | Static or periodically updated models. | Continuous adaptation and learning from dynamic threat environments. |
| Speed | Faster than humans at detection but waits for human intervention to act. | Machine speed execution: isolates compromised assets, updates rules, or deploys deception autonomously. |
| Examples in Cybersecurity | Malware detection using ML, anomaly detection in SIEM logs, phishing classification. | Autonomous SOC agent that hunts threats, isolates compromised endpoints, patches vulnerabilities, and deploys countermeasures. |
| Limitations | Limited context, requires orchestration tools (SIEM/SOAR) + human judgment. | Risk of overreach (false positives leading to disruptions), accountability concerns, adversarial manipulation. |
Sofia Ali, Associate Director & Principal Analyst, QKS Group, has a warning for those opting for Agentic AI-driven security operations: “Agentic AI does let security teams react at machine speed, but every move it makes without human checks adds risk. The safest approach is to pair its fast, autonomous actions with clear human oversight.”
Agentic advantages
As we can clearly glean from the chart, integrating AI into the security stack is good, but integrating Agentic AI is better. It is especially useful for organizations with a complex environment of multiple endpoints and integration with multiple SaaS apps. The bigger size means a higher number of alerts and false positives. Agentic AI can provide the following advantages:
- Speed: An Agentic-AI equipped security stack can respond at machine speed before threats such as ransomware or ATO campaigns escalate.
- Complexity: An Agentic AI-equipped security system can navigate hybrid, multi-cloud, and SaaS-heavy environments with invisible attack paths.
- Human bandwidth: The autonomy offered by Agentic AI-equipped security can automate tasks and reduce reliance on overstretched SOC analysts.
Some use cases:
Here are some tasks that an Agentic AI-infused security can do:
- Autonomous Threat Hunting: It can continuously scan logs, network flows, and SaaS connectors for anomalies without waiting for analysts.
- Real-Time Incident Response: It can automatically isolate compromised endpoints or disabling suspicious accounts.
- Supply Chain Defense: It can monitor API tokens, plugins, and third-party services for abuse.
- Adaptive Vulnerability Management: It can identify and patch critical vulnerabilities before attackers exploit them.
- Insider Threat Mitigation: It can spot credential misuse or privilege creep and acting before damage occurs.
- Active Defense & Deception – deploying honeypots, decoys, and diversionary tactics to slow attackers down.
But hold up…
Even as Agentic AI will reduce SOC workload, as it is the way with any modern technology, it introduces new risks of its own. First and foremost is the autonomy. While Agentic AI may handle tasks on its own, but there is a danger of misdiagnosing. Therefore, a human in the loop is essential to ensure security without any inadvertent goof-ups. Apart from this, the most risks are the ones we usually associate with any AI. The agents also integrate with various systems and external and internal data sources. This factor expands the attack surface. In addition, a chain of command needs to be established to ensure proper vetting and ownership of the results. It will also face the AI-specific threats like prompt injection and data poisoning.
Final word
Agentic AI is capable to make life very easy for SOC personnel, but it is not a cure-all solution. It is also vulnerable to most threats to any LLM and AI. The ideal strategy is to keep a human in the loop for critical decisions when needed. The deployment should be in incremental stages to allow fine-tuning and maximum usefulness. As things stand, the best bet is on human-assisted Agentic AI to ensure a better security posture for your organization.