AI vs. AI: Why It Takes AI Security to Stop AI Attacks

How AI Has Changed Cyberattacks

From Generative AI to Agentic AI

Generative AI answers questions and produces text, code, and images. Agentic AI, by contrast, can plan on its own toward a given goal and complete multi-step tasks using the tools it needs. In security operations, for instance, it goes beyond merely summarizing logs to run an entire sequence as a single flow—gathering relevant information, analyzing threats, and then recommending response measures. In short, AI is evolving from "a tool that generates answers" into "an agent that gets work done"—and the same shift is playing out in cyberattacks.

Automation and Speed Are What Define AI Attacks

The rise of AI hasn't changed the fundamentals of cyberattacks. Techniques like vulnerability exploitation, malware execution, account takeover, privilege escalation, and lateral movement are still extensions of established methods. What's changed is how attacks are carried out. AI can rapidly analyze vast amounts of data, automate repetitive tasks, and decide its next move based on the results. Attackers use this to find vulnerabilities faster, generate a wider range of attack scenarios, and run the same attack at scale.

In the end, the essence of AI-powered attacks lies not in a new technique, but in the automation and acceleration of existing ones.

How AI Makes Attacks More Sophisticated

AI can dramatically increase both the efficiency and the scale of attacks. The key changes are as follows.

① Automating vulnerability discovery and attack preparation
AI analyzes publicly available vulnerability data and technical documentation to rapidly compile information it can use in an attack. As preparation time shrinks, so does the window between a new vulnerability's disclosure and a real-world attack.

② Running large-scale attacks repeatedly
AI can repeat the same task without rest. It can pursue multiple attack strategies at once and, learning from failed attempts, apply new methods to keep the attack going.

③ Sharpening tailored attacks
AI can draw on publicly available information to craft phishing messages aimed at a specific organization or user, or to build attack scenarios suited to the situation. Down the road, it could evolve to sustain a conversation based on how the target responds.

AI Also Becomes a New Attack Surface

AI automates attacks, but it also becomes a new target of them.

Once enterprises start using AI agents in their operations, what systems an AI accesses, what privileges it holds, and what tasks it performs all become new items to manage from a security standpoint. An attacker might, for example, feed an AI malicious instructions to trigger unintended actions, or hijack an AI agent's privileges to access systems while posing as a legitimate user.

Going forward, then, enterprises need not only ways to leverage AI, but also a security framework for operating AI itself safely.

Why It Takes AI Security to Counter AI Attacks

The Limits of Human-Centered Security Operations

Conventional security operations have relied on people to review security events, analyze whether they pose a threat, and then respond.

But as AI-powered attacks drive up the speed and scale of attacks, the volume of security events that people must handle rises with them.

Security teams have to sift through countless logs, gather information from multiple security systems, judge whether an attack is underway, and set response priorities. It's a process that demands deep expertise—and considerable time and effort.

When attacks are automated by AI but defense stays at human speed, the gap between the two can only keep widening.

AI Security Is a New Operating Model

AI security isn't about adding generative AI features to existing security products. The essence is using AI to boost the speed and efficiency of security operations as a whole. AI can analyze massive volumes of security data, establish an attack's context by connecting it with threat intelligence, and propose response priorities. With AI handling repetitive analysis and investigation, security experts can concentrate on the judgment calls and strategy that matter most.

In this sense, AI security isn't a single feature—it's a technology that transforms the security operations model itself.

From Detection to AI-Powered Security Operations

Security is shifting from simply detecting known threats to identifying and responding to new ones as fast as possible. Meeting that bar means unifying data across endpoint, network, cloud, CPS, and other domains, and letting AI analyze it to rapidly trace an attack's path and scope of impact. It also means putting a structure in place where security experts validate AI's analysis and recommended actions before making the final decision.

In the end, security in the AI era is advancing toward pairing the strengths of people and AI to raise operational efficiency and response speed. In an age where attackers use AI, defenders have to use it as well. AI security is no longer an optional capability—it's becoming a new way to operate security.

How AI Security Operations Are Changing

AI-powered security operations aren't merely a matter of faster analysis. Their real value is in reshaping how security is operated—moving from a human-centered approach to one built on collaboration between AI and experts.

How Agentic AI Changes Security Operations

Conventional AI stayed in a support role—answering analysts' questions or summarizing logs.

Agentic AI goes further: it can plan and execute, on its own, the tasks needed to reach a given goal. In security operations, it's evolving to run an entire sequence as a single flow—detecting a threat, collecting and analyzing the related information, and then recommending how to respond. When a new security event occurs, for instance, AI can pull the relevant logs and threat information, analyze the attack's flow and scope of impact, and propose response priorities. Analysts then review AI's findings and make the final response call. In this way, AI takes on repetitive operational work while security experts shift toward focusing on judgment and decision-making.

Multiple AI Agents Operate Security Together

Going forward, security operations are expected to evolve toward a model where multiple AI agents—each owning a different role—collaborate, rather than one AI doing all the work. One agent might analyze security events, another check threat intelligence, and another recommend response measures. Each agent's output feeds into a single operational flow, enabling faster and more consistent security response.

The point of this structure isn't to replace people's work. By offloading repetitive tasks to AI, it frees security experts to focus on the higher-stakes work of judgment and strategy.

Collaboration Between AI and Experts Is Key

AI is advancing fast, but ultimate responsibility for security operations still rests with people.

Real-world response has to weigh more than technical risk—business impact, system criticality, regulation, and compliance all factor in. That kind of judgment is hard to make with AI alone.

So the AI security of the future will hinge on a human-in-the-loop structure: AI supports analysis and response, and security experts verify it and make the final call.

AI's role isn't to replace people—it's to help them judge faster and more accurately.

How AhnLab Is Preparing for the AI Attack Era

AhnLab sees AI not as one more feature added to existing products, but as a core technology that connects and advances security operations as a whole. With that in mind, it is developing an AI-powered security operations framework built around its own AI platform, AhnLab AI PLUS.

AhnLab AI PLUS: An AI Platform for Security Operations

AhnLab AI PLUS isn't a standalone security product. It's a unified AI platform that connects AhnLab's various security products and services—endpoint, network, cloud, CPS, and more—through AI, bringing intelligence across the whole of security operations.

AhnLab is structuring over 30 years of accumulated malware analysis data, threat intelligence, and incident response experience so that AI can draw on it. On this basis, AI analyzes security events, understands the context behind threats, and proposes how to respond. The essence of AhnLab AI PLUS, then, lies not simply in adopting the latest AI models, but in enabling AI to make effective use of the security data AhnLab has accumulated over the years.

How AI Supports Security Operations

In AhnLab AI PLUS, a single AI doesn't carry out every task.

AI agents responsible for different roles—security event analysis, threat intelligence, response support, and more—collaborate to form one security operations flow.

For example, when a new security event occurs, AI can support the following process:

• Event and log analysis
• Checking related threat intelligence
• Analyzing the attack flow and scope of impact
• Risk assessment
• Recommending response priorities and measures

Based on these findings, security experts can more quickly decide whether a response is required.

Extending AhnLab AI PLUS Across the Entire Portfolio

AhnLab AI PLUS isn't AI applied to one product—it's a unified AI platform that connects AI capabilities across AhnLab's various security products and services.

Today, AI capabilities are being expanded across multiple products to enable AI-powered security operations, with two notable examples: AhnLab XDR's AI security assistant, "Annie," and AhnLab EDR's "AI Insight."

Annie is a conversational AI security assistant that lets security teams query threat information in natural language and quickly pull up related events and analysis. By cutting the time spent on repetitive lookups and analysis, it frees them to focus more on threat analysis and response.

AI Insight is an AI-powered analysis feature that comprehensively analyzes events detected by AhnLab EDR and delivers—as a single analysis report—the attack intent, threat context, MITRE ATT&CK-based TTPs, threat intelligence, recommended response direction, and more. With it, security teams can quickly grasp what complex events mean and prioritize their response more efficiently.

In this way, AhnLab isn't confining AI capabilities to specific products; it's continuously extending them across its range of security products and services, all centered on AhnLab AI PLUS, to advance its AI-powered security operations framework.

AhnLab's AI Edge Comes from Security Domain Expertise

The performance of AI security isn't determined by the AI model alone. How deeply it understands the real security environment—its domain-specific data and operational experience—is what drives the AI's accuracy and usefulness.

Building on more than 30 years of accumulated security expertise and threat analysis experience, AhnLab is advancing a domain-specific AI purpose-built for security. The core of AhnLab's AI strategy is enabling AI to understand the context of real security threats and carry out more accurate detection, analysis, and response. This edge rests on four foundations.

① Security Domain Expertise: AhnLab draws on more than 30 years of malware analysis data, incident response experience, and security operations know-how as the foundation for AI training and analysis. This lets AI move beyond merely analyzing data to understand the context of the real security environment and make more sophisticated judgments.

② Threat Intelligence: The threat intelligence AhnLab has continuously accumulated is a core asset that helps AI analyze attack techniques, threat behavior, and risk levels more accurately, and prioritize the response.

③ Unified Security Platform: By connecting data from across security domains—endpoint, network, cloud, CPS, and more—into a single operational flow, it gives AI an environment to analyze not just individual events but the full flow and scope of impact of an attack.

④ Trustworthy AI Operations: The wider AI's use becomes, the more accuracy and reliability matter. AhnLab focuses on building a trustworthy AI-powered security operations environment—raising AI's autonomy while applying guardrails and an expert verification framework.

Ultimately, the core of AhnLab's AI advantage isn't adopting the latest AI models. What sets AhnLab apart is combining security domain expertise, threat intelligence, a unified security platform, and a trustworthy AI operations framework so that AI can understand and put to use the real security environment.

A New Kind of Security Competitiveness Is Emerging in the AI Era

AI is rapidly reshaping the speed and scale of cyberattacks. Attacks will only grow more automated and intelligent, and security operations will inevitably have to evolve in response.

Enterprises must now see AI not merely as something to protect or control, but as a core technology for strengthening their security operations. In an era where attackers leverage AI, defenders must use AI too—to speed up detection, analysis, and response.

AI, of course, doesn't replace security experts. Its strength is in rapidly analyzing vast amounts of data and taking on repetitive work, while the final judgment and decisions remain the domain of security experts. Security operations are likely to move toward a human-in-the-loop model that combines the respective strengths of AI and people.

In step with this shift, AhnLab is advancing AI not as a standalone feature but as a platform technology that connects security operations as a whole. Centered on AhnLab AI PLUS, it continues its R&D—uniting security domain expertise, threat intelligence, and a unified security platform—so that AI delivers real-world value in everyday security operations.

In the end, security competitiveness in the AI era lies in considering both how safely AI is managed and how effectively it is used. Through an agentic AI-based security operations environment that combines AI with security expertise, AhnLab will continue to advance its AI security capabilities so enterprises can respond to threats faster and more accurately.

FAQ: Key Questions About AI Attacks and AI Security