Artificial Intelligence (AI) is fueling more sophisticated and autonomous cyber threats, requiring defenders to adopt equally advanced tools to keep pace. One of the most promising developments is agentic AI, which enables autonomous agents to detect, respond to, and remediate threats with minimal human intervention.
Although AI has long been part of cybersecurity, agentic AI marks a shift toward intelligent automation that interprets context, makes decisions, and acts on predefined goals.
Whether you’re evaluating emerging technologies or actively integrating AI into your security stack, understanding how agentic AI works – and where it fits – will help you strengthen your security posture in an era of increasingly intelligent threats.
In this Q&A with Dan Richings, Senior Vice President of Global Customer Success and Solutions at Adaptiva, we explore how agentic AI differs from traditional AI, the role it plays in modern vulnerability management, and the considerations organizations must weigh before deployment.
Agentic AI is a buzzworthy concept right now. How is agentic AI different from traditional automation or AI used in cybersecurity tools today?
Agentic AI uses autonomous agents to handle specific tasks, focusing on assigning the right tasks to AI. These specialized models are designed for targeted functions, outperforming standard AI models that rely on general prompts.
What sets agentic AI apart from traditional AI is its ability to act with a degree of independence and intent. These agents are programmed to interpret events, make decisions, and take action without needing constant input from users.
Instead of simply reacting to direct prompts, agentic AI responds dynamically to real-world events based on predefined goals and instructions. This shift allows for more intelligent automation, reducing the need for continuous oversight.
What are the biggest challenges security teams face today when it comes to vulnerability detection and remediation, and how can agentic AI help solve them?
Security tools continue to integrate AI capabilities in new and innovative ways, and agentic AI is just one example of how these tools are steadily improving. The core challenge, however, is that threats and vulnerabilities are evolving just as rapidly, also leveraging AI to execute sophisticated attack flows. While staying ahead of these emerging threats remains a major challenge for security teams, AI is helping defenders close gaps that are inherent in many manual processes.
For example, in vulnerability remediation, AI’s ability to understand the intent behind code, rather than simply recognizing a file by its signature, is critical to blocking attacks before they occur.
Can you walk us through how an agentic AI system might autonomously detect and remediate a vulnerability in real time?
The AI agent monitors the system, ingests threat intelligence feeds from vulnerability management tools, and uses this data to identify disclosed vulnerabilities by sensing, reasoning, and acting.
Agentic AI systems leverage a predefined set of contextual reasoning and decision-making rules to evaluate potential threat behaviors, such as how executables might act when launched, enabling automated decision-making for remediation. When determining the appropriate response to malware, the agent assesses whether to patch the software, isolate it, block network traffic, delete and clean the file, or take any other necessary autonomous action before threat actors can execute their intended purpose.
What role does human oversight play when deploying agentic AI in a security environment?
When it comes to any type of AI, we should remember that while AI is a powerful tool, it is not infallible. We still need humans to define boundaries in which AI can operate, like managing what tasks agents perform, and what they are allowed to do by setting guardrails and limitations.
What are the security considerations organizations should keep in mind when adopting agentic AI?
For security teams, it’s essential to fully understand each agent’s intended function and behavior, and to have a failover plan in place should it not perform as expected. As mentioned earlier, AI is not infallible and should not be trusted wholeheartedly without human validation.
AI requires training, whether from the organization’s environment and system behaviors or from the security company responsible for developing the AI tool. In fact, AI training agent roles are increasingly becoming more common, and I think this is the way that the industry is progressing.
Looking ahead, how do you see agentic AI shaping the future of autonomous endpoint management and cybersecurity more broadly?
AI is a double-edged sword because we’re using AI in cyber defense, but malicious actors are also using autonomous agents to execute attacks. Overall, cyber defense is becoming increasingly more autonomous, and AI agents will become more responsible for maintaining healthy systems.
Thank you, Dan, for sharing your insights on agentic AI!
