How Kaspersky solutions protect against AI threats

Popular AI applications and services in organizations

Application Control and Web Control

Kaspersky Endpoint Detection and Response

Kaspersky Threat Intelligence Portal

Kaspersky Scan Engine

AI agents and associated tooling are becoming increasingly common in corporate infrastructure. Agents can perform a range of actions with the user’s privileges, such as accessing data, running commands, and interacting with external services. This creates new security risks, especially when these tools are used without the security team’s oversight or well-designed policies.

Detecting, tracking, and controlling AI agents is becoming one of the most pressing tasks for security teams today. We closely follow technological developments and continuously update our solutions to respond to new threats in a timely manner amid a rapidly changing technology landscape. This post describes new expertise built into Kaspersky products that helps counter the threats posed by the proliferation of AI agents. We approach this problem from several angles.

First, you need to thoroughly understand the threat you are dealing with. To this end, we added profiles to the Kaspersky Threat Intelligence Portal that describe various AI agents and how attackers exploit them. At their core, AI agents are programs (processes) that run in the operating system on endpoints and can also launch other processes. This means that Endpoint Detection and Response solutions are among the key endpoint controls for this threat. These solutions detect the presence and launch of AI agents, track their activity, and identify potentially dangerous behavior. When needed, they also take action, such as stopping a suspicious process, blocking the activity, or isolating the host, to reduce the likelihood of an incident escalating. For this purpose, we developed specially adapted detection rules.

You can use Kaspersky Endpoint Security, now also available as Kaspersky Next , to control and restrict the launch of AI agents. This solution uses two components to detect AI agents and control how they are used. Application Control categorizes the application processes launched on endpoints, and Web Control manages access to specific categories of web resources for users on endpoints.

Finally, even harmless AI agents can contain malicious instructions (skills), such as those downloaded from unsafe external sources. Kaspersky Scan Engine analyzes installed applications to detect these threats and also checks LLM files for malicious code. Let’s take a closer look at these solutions and technologies.

Tools from Anthropic and OpenAI take the top spots in the September 2026 ranking of AI agents by popularity among Kaspersky users. This list is based on telemetry from Kaspersky solutions. It shows the number of users who had the relevant AI vendors’ products installed at the time of the study.

The data shows that agentic systems, especially AI agents for software development, are the most popular AI applications. The TOP 10 also includes an application for running large language models locally (LM Studio) and a voice note-taking app (Wispr Flow).

According to open sources, these are the most widely used AI services in 2026:

While analyzing the most popular and widely used apps, our experts also noticed a sharp rise in vulnerable versions of AI-related software and services . From 2025 to date, more than 2500 vulnerabilities have been published:

Number of published vulnerabilities in LLMs, AI tools, and plugins with similar functionality, 2025–2026 ( download )

* Data for previous quarters was updated and may differ slightly from previously published data.

To enforce the organization’s security policy for AI agents and other AI applications on the corporate network, Kaspersky products offer application control based on KL categories . These categories classify installed software and give you granular control over which approved applications can run in the organization.

Application Control analyzes the processes launched on endpoints, tracking which AI agents are running in corporate systems at any given moment, on which hosts, and under which accounts. Application Control also shows how long a given program has been in use.

This makes it easier for the Kaspersky Security Center (KSC) administrator to control applications. Administrators can use KL categories to allow or block AI agents inside the company for specific groups of employees.

Here is what this functionality looks like to KSC administrators:

KL categories available when configuring Application Control

For details on how to configure Application Control in KSC, follow this link .

In Kaspersky Endpoint Security (KES), Application Control can operate in two modes:

Denylist . In this mode, Application Control lets users run any applications except those blocked by Application Control rules. This is the default mode.

Allowlist. In this mode, Application Control blocks users from running any applications except those explicitly allowed by Application Control rules.

Below are the steps to configure Application Control to block AI tools.

Configuring Application Control for the AI Tools category in KES

With this setup, when a user tries to launch an AI agent, they get a notification and the agent doesn’t start.

KES notification about an attempt to launch an AI agent

For more on how to configure Application Control in KES, follow this link . Our public portal also lets you explore information on KL categories and the applications that fall under them, in particular details on how AI agents are categorized and how popular they are .

AI Tools KL category

The View applications drop-down list shows products for a specific vendor.

Similarly, Web Control lets you manage user access to web resources. If a user tries to open a denylisted website, KES blocks access and shows a warning.

KES notification about an attempt to access a prohibited web resource

When configuring access, the administrator can block websites that provide AI as a service.

For more on configuring Web Control, follow this link . The help portal also provides information on website categories that can be assigned to resources to configure access control.

To an EDR solution, an AI agent is a regular process. It has its own executable, a tree of child processes, and network communications, all of which need to be monitored for suspicious activity. An attacker can use an AI agent as an automation tool. An AI agent installed as a legitimate user tool can also be leveraged in a ClickFix attack, where it executes a malicious instruction received from an untrusted source, such as a document, web page, or email. That said, unsafe actions by an agent do not always indicate an attack. There may be no malicious intent in the chain at all. An incident can result from what looks like an ordinary instruction from a user who doesn’t fully understand the consequences of having it executed. The mere fact that an agent was launched does not reveal whether it is being used in an attack. That is why Kaspersky EDR (KEDR) uses both conventional behavioral rules and rules written and adapted to detect AI tools. The latter identify risky agent actions and settings, and help determine which AI tool was involved in suspicious activity.

Conventional KEDR rules detect the vast majority of familiar malicious techniques, including when performed by an AI agent. A suspicious PowerShell launch, persistence via Task Scheduler, or an attempt to steal credentials leaves clear, identifiable traces. These traces look the same no matter what initiated the actions. The rules analyze launch parameters, process relationships, affected files, network activity, and other telemetry events.

For example, in a recent Armored Likho campaign , we found signs that AI had been used to create loader and dropper samples, which were later compiled with NSIS (Nullsoft Scriptable Install System). The key telltale artifacts were icons in the scripts and excessive comments on every function, which is uncharacteristic of human-written code. In addition, the group continues to generate new modules for its BusySnake Stealer and BusySnake RAT samples. For example, the attackers used AI to develop a module that runs multiple checks for virtual environment artifacts and calculates a score. Depending on the score, the sample either runs or exits with code 0. Even though the initial access samples, payloads, and their modules were AI-generated, the commands they execute via rundll32 and PowerShell are detected by the shell_creation_by_rundll32 and suspicious_powershell_cmd_or_script_spawning rules.

So, for detecting an attack like this, it is not important who wrote the loader, the payload, or its modules: they are given away by the commands they execute, process relationships, and other traces of activity in the system. AI can be involved both in preparing the code and in carrying out the attack directly on the device.

In the latter case, the attacker uses the agent to carry out their tasks and lets it choose and run the required commands within the privileges it has. For effective protection, it is critical to link suspicious actions back to the AI tool and the conditions under which it operates. In KEDR, several specialized rules handle this task.

We have created separate rules for popular AI agents, ai_agent_*_access_to_sensitive_file_** , where * is the tool name and ** is the operating system. They generate alerts when an agent accesses files that contain credentials, tokens, or keys.

The ai_agent_dangerous_execution_mode rule detects agents launched with auto-approval of all actions (so-called YOLO mode) or with the sandbox disabled. These settings do not indicate an attack on their own, but they let the agent perform dangerous actions without user confirmation.

The suspicious_mcp_server_artifact rule triggers when MCP server scripts are created or modified in hidden, temporary, or cache directories. Attackers can place an MCP server in these locations to quietly connect it to the agent: through the MCP server, they can pass instructions to the agent, call third-party tools, and access the user’s data.

The llm_api_access_by_unusual_apps rule generates an alert when an application on Windows, Linux, or macOS connects to the API of a known external LLM service, unless this activity falls under predefined exclusions. This helps spot model access by applications that do not normally behave this way.

Some Kaspersky EDR rules tag events for investigation but do not generate alerts. For example, the ai_agent_**_execution rules flag launches of known agents on Windows, Linux, and macOS. The local_ai_model_artifact rule records the creation or modification of AI model files in typical application, runtime, and cache directories. These events reveal AI tools on the device, including tools that run on local models and never call an external API.

The Kaspersky Threat Intelligence Portal offers a large number of software profiles, including profiles of AI agents. You can sort them by the ai_ prefix:

AI agent profiles on Kaspersky TIP

The profile of an AI agent such as Claude Desktop includes a detailed description of the software, the privilege level required to run it, supported platforms, and threat actor groups known to use it. It also lists tactics and techniques mapped to the MITRE ATT&CK framework, with descriptions of the procedures the tool can be used for.

Claude profile on Kaspersky TIP

The description also lists ways to detect the software through standard system auditing mechanisms:

Windows: events from standard audit logs and Sysmon events

Linux: syscall audit events and Sysmon events

Network auditing: a set of protocols and the traffic generated by the software

The detection section lists the main artifacts the software leaves in the system: command lines, possible launch parameters, names of the services and scheduled tasks it creates, and the key network artifacts.

Detection section in the Claude profile

In December 2025, the Kaspersky Threat Intelligence Portal added Hunt Hub (HH), a repository of detection rules. This is a unique Kaspersky project, and we developed it in response to growing market demand.

This functionality is available in the Threat Landscape section of the portal. It gives analysts access to detection rules: their descriptions and their mapping to MITRE ATT&CK tactics and techniques, as well as to the associated cybercriminal groups.

In the Hunt Hub section, customers can explore the detection logic of EDR, NDR, and NGFW products, including the logic for detecting AI agents.

Rules in the Hunt Hub repository

The rule card provides:

Detailed description

Rule’s severity level, confidence level, and alert format

Description of possible false positives

Platforms the rule can run on

Tactics, techniques, and sub-techniques covered by the rule

Groups that use the technique covered by the rule

Detection logic that shows what exactly triggers the rule

Detection rule card in Hunt Hub

Skills are a key component of the AI agent ecosystem. They’re prewritten sets of natural-language instructions that extend agent functionality. We previously covered the risks that malicious skills pose to agent users. A skill can require the usage of additional programs and services, come with helper scripts (for example, written in Python), or contain a malicious command embedded right in the instruction text. This means that adding skills to an originally harmless AI agent opens up several new ways for malicious code to enter the system. To prevent this, any skills obtained from external sources must be scanned. Kaspersky Scan Engine is well suited for this task. This is a cyberthreat detection and response solution that seamlessly integrates with various applications. It can detect malicious code in agent skills, associated scripts, and in files generated by running agents. Detection of a malicious agent skill in Kaspersky Scan Engine Trained LLMs come in many formats. Some of them (PTH, PT, PKL, KERS, and others) also store parameters and weights (training datasets). These files can also contain malicious code, so it’s critical to scan them before running and using them. Kaspersky Scan Engine scans potentially dangerous LLM formats for infected data. It can deserialize and analyze models to detect malicious code regardless of model size. Detection of an LLM with malicious code in Kaspersky Scan Engine

Detection of a malicious agent skill in Kaspersky Scan Engine

Detection of an LLM with malicious code in Kaspersky Scan Engine

AI agents are rapidly making their way into corporate environments: they help employees work faster and take on a wide range of tasks, but they also open up new opportunities for attackers. To take advantage of this technology without compromising security, organizations need to build multi-layered protection and minimize risks. A range of Kaspersky solutions can help with this:

Kaspersky TIP keeps you up to date on current scenarios in which attackers use AI agents, and its Hunt Hub section provides rules for detecting suspicious AI agent activity.

Kaspersky EDR detects AI agents in the infrastructure and monitors their activity on endpoints. If it detects potentially dangerous actions, the product can log the event, notify the SOC, and take response actions, such as stopping the process or isolating the host.

Application Control in Kaspersky products can fully restrict the use of AI agents within the company by using application categorization. The Web Control component can restrict access to AIaaS web resources.

Kaspersky Scan Engine can be integrated into various company processes to detect malicious code in any files, including AI agent skills and LLMs.

When companies combine the tools described above in a comprehensive approach, they can adopt AI agents in an informed way: with an understanding of the associated risks and effective means of minimizing them.

Proactive protection

Alexander Kirichenko

Deploying AI agents securely: how Kaspersky products mitigate AI threats

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组织中流行的 AI 应用和服务

应用程序控制和 Web 控制

卡巴斯基端点检测与响应

卡巴斯基威胁情报门户

卡巴斯基扫描引擎

人工智能代理及其相关工具在企业基础设施中正变得越来越普遍。代理可以利用用户的权限执行一系列操作，例如访问数据、运行命令以及与外部服务交互。这会带来新的安全风险，尤其是在缺乏安全团队监督或完善策略的情况下使用这些工具时。

检测、追踪和控制人工智能代理正成为当今安全团队面临的最紧迫任务之一。我们密切关注技术发展，并不断更新解决方案，以便在瞬息万变的技术环境中及时应对新的威胁。本文将介绍卡巴斯基产品中内置的全新功能，这些功能有助于应对人工智能代理激增带来的威胁。我们将从多个角度探讨这一问题。

首先，您需要彻底了解所面临的威胁。为此，我们在卡巴斯基威胁情报门户中添加了描述各种人工智能代理及其攻击方式的配置文件。人工智能代理本质上是在终端操作系统中运行的程序（进程），它们还可以启动其他进程。这意味着端点检测与响应 (EDR) 解决方案是应对此类威胁的关键端点控制措施之一。这些解决方案可以检测人工智能代理的存在和启动情况，跟踪其活动，并识别潜在的危险行为。必要时，它们还会采取措施，例如停止可疑进程、阻止其活动或隔离主机，以降低事件升级的可能性。为此，我们开发了专门定制的检测规则。

您可以使用 Kaspersky Endpoint Security（现已更名为 Kaspersky Next）来控制和限制 AI 代理的启动。该解决方案使用两个组件来检测 AI 代理并控制其使用方式。“应用程序控制”组件对端点上启动的应用程序进程进行分类，“Web 控制”组件则管理端点用户对特定类别 Web 资源的访问权限。

最后，即使是看似无害的人工智能代理也可能包含恶意指令（技能），例如从不安全的外部来源下载的指令。卡巴斯基扫描引擎会分析已安装的应用程序以检测这些威胁，还会检查LLM文件中是否存在恶意代码。让我们更详细地了解这些解决方案和技术。

根据卡巴斯基用户2026年9月发布的AI代理受欢迎程度排名，Anthropic和OpenAI的工具位列榜首。该排名基于卡巴斯基解决方案的遥测数据，显示了在调查期间安装了相关AI供应商产品的用户数量。

数据显示，智能体系统，尤其是用于软件开发的AI智能体，是目前最受欢迎的AI应用。排名前十的应用还包括一款用于在本地运行大型语言模型的应用（LM Studio）和一款语音笔记应用（Wispr Flow）。

根据公开资料显示，以下是2026年最广泛使用的AI服务：

在分析最流行、使用最广泛的应用程序时，我们的专家还注意到，人工智能相关软件和服务的漏洞版本数量急剧增加。自2025年以来，已公布的漏洞超过2500个：

2025-2026 年 LLM、AI 工具和具有类似功能的插件中已公布的漏洞数量（下载）

* 前几个季度的数据已更新，可能与之前公布的数据略有不同。

为了在企业网络中强制执行针对人工智能代理和其他人工智能应用程序的安全策略，卡巴斯基产品提供基于 KL 分类的应用程序控制功能。这些分类对已安装的软件进行分类，使您可以精细控制哪些已批准的应用程序可以在组织中运行。

应用程序控制功能会分析端点上启动的进程，跟踪企业系统中任何给定时刻正在运行的 AI 代理、运行在哪些主机上以及使用哪些帐户。应用程序控制功能还会显示特定程序的使用时长。

这使得卡巴斯基安全中心 (KSC) 管理员能够更轻松地控制应用程序。管理员可以使用 KL 类别来允许或阻止公司内部特定员工群体使用 AI 代理。

以下是KSC管理员看到的该功能：

配置应用程序控制时可用的 KL 类别

有关如何在 KSC 中配置应用程序控制的详细信息，请点击此链接。

在卡巴斯基安全软件（KES）中，应用程序控制可以以两种模式运行：

拒绝列表。在此模式下，应用程序控制允许用户运行除被应用程序控制规则阻止的应用程序之外的任何应用程序。这是默认模式。

允许列表。在此模式下，应用程序控制会阻止用户运行除应用程序控制规则明确允许的应用程序之外的任何应用程序。

以下是配置应用程序控制以阻止 AI 工具的步骤。

在 KES 中配置 AI 工具类别的应用程序控制

在这种设置下，当用户尝试启动 AI 代理时，他们会收到通知，但代理不会启动。

KES 发出通知，称有人试图启动人工智能代理。

如需了解如何在 KES 中配置应用程序控制，请点击此链接。我们的公共门户网站还允许您浏览 KL 类别及其下属应用程序的信息，特别是有关 AI 代理的分类方式及其受欢迎程度的详细信息。

AI 工具 KL 类别

“查看应用程序”下拉列表显示特定供应商的产品。

同样，Web 控制功能允许您管理用户对 Web 资源的访问权限。如果用户尝试打开被列入黑名单的网站，KES 会阻止访问并显示警告。

KES 通知：有人试图访问被禁止的网站资源

配置访问权限时，管理员可以屏蔽提供人工智能服务的网站。

如需了解更多关于配置 Web 控制的信息，请点击此链接。帮助门户还提供了有关网站类别的信息，这些类别可以分配给资源以配置访问控制。

对于 EDR 解决方案而言，AI 代理是一个常规进程。它拥有自己的可执行文件、子进程树以及网络通信，所有这些都需要被监控，以发现可疑活动。攻击者可以将 AI 代理用作自动化工具。即使是作为合法用户工具安装的 AI 代理，也可能被用于 ClickFix 攻击，执行从不受信任的来源（例如文档、网页或电子邮件）接收的恶意指令。也就是说，代理的不安全行为并不总是意味着存在攻击。整个流程中可能根本没有恶意意图。事件可能源于看似普通的用户指令，而用户可能并不完全了解执行该指令的后果。仅仅启动代理这一事实并不能表明它是否被用于攻击。因此，卡巴斯基 EDR (KEDR) 同时使用传统的行为规则以及专门编写和调整的 AI 工具检测规则。后者可以识别风险代理行为和设置，并帮助确定哪个 AI 工具参与了可疑活动。

传统的 KEDR 规则可以检测到绝大多数常见的恶意技术，包括由 AI 代理执行的恶意操作。可疑的 PowerShell 启动、通过任务计划程序进行的持久化操作或窃取凭据的尝试都会留下清晰可识别的痕迹。无论这些操作是由什么发起的，这些痕迹看起来都一样。这些规则会分析启动参数、进程关系、受影响的文件、网络活动和其他遥测事件。

例如，在最近一次 Armored Likho 攻击活动中，我们发现有迹象表明，攻击者利用人工智能创建了加载器和投放器样本，这些样本随后使用 NSIS（Nullsoft Scriptable Install System，Nullsoft Scriptable Install System，Nullsoft Scriptable Install System）进行了编译。关键的蛛丝马迹是脚本中的图标以及每个函数上的大量注释，这与人类编写的代码截然不同。此外，该组织还在持续为其 BusySnake Stealer 和 BusySnake RAT 样本生成新的模块。例如，攻击者利用人工智能开发了一个模块，该模块会对虚拟环境进行多项检查并计算得分。根据得分，样本要么运行，要么以代码 0 退出。尽管初始访问样本、有效载荷及其模块都是由人工智能生成的，但它们通过 rundll32 和 PowerShell 执行的命令会被 shell_creation_by_rundll32 和 suspect_powershell_cmd_or_script_spawning 规则检测到。

因此，对于此类攻击的检测，加载器、有效载荷或其模块的编写者并不重要：它们执行的命令、处理关系以及系统中的其他活动痕迹都会暴露其真实身份。人工智能既可以参与代码编写，也可以直接在设备上执行攻击。

在后一种情况下，攻击者利用代理执行其任务，并允许代理在其拥有的权限范围内选择和运行所需的命令。为了有效防护，至关重要的是将可疑行为追溯到人工智能工具及其运行条件。在 KEDR 中，有几条专门的规则可以处理这项任务。

我们为常用的AI代理创建了单独的规则，规则名称为ai_agent_*_access_to_sensitive_file_**，其中*代表工具名称，**代表操作系统。当代理访问包含凭据、令牌或密钥的文件时，这些规则会生成警报。

ai_agent_dangerous_execution_mode 规则用于检测以自动批准所有操作（即所谓的 YOLO 模式）或禁用沙箱模式启动的代理。这些设置本身并不表明存在攻击，但它们允许代理在未经用户确认的情况下执行危险操作。

当在隐藏目录、临时目录或缓存目录中创建或修改 MCP 服务器脚本时，可疑的 mcp_server_artifact 规则将被触发。攻击者可以将 MCP 服务器放置在这些位置，从而悄悄地将其连接到代理：通过 MCP 服务器，他们可以向代理传递指令、调用第三方工具并访问用户数据。

当 Windows、Linux 或 macOS 上的应用程序连接到已知外部 LLM 服务的 API 时，除非此活动符合预定义的排除条件，否则 llm_api_access_by_unusual_apps 规则会生成警报。这有助于发现那些通常不会以这种方式访问​​模型的应用程序。

卡巴斯基EDR规则中的一些会标记事件以供调查，但不会生成警报。例如，ai_agent_**_execution规则会标记已知代理在Windows、Linux和macOS上的启动事件。local_ai_model_artifact规则会记录典型应用程序、运行时和缓存目录中AI模型文件的创建或修改。这些事件会揭示设备上的AI工具，包括那些运行在本地模型上且从不调用外部API的工具。

卡巴斯基威胁情报门户网站提供大量软件配置文件，包括人工智能代理的配置文件。您可以按 ai_ 前缀对它们进行排序：

卡巴斯基TIP上的AI代理配置文件

类似 Claude Desktop 这样的 AI 代理的配置文件包含软件的详细描述、运行所需的权限级别、支持的平台以及已知使用该软件的威胁行为者组织。它还列出了与 MITRE ATT&CK 框架相对应的战术和技术，并描述了该工具可用于执行的操作流程。

克劳德在卡巴斯基TIP上的个人资料

该描述还列出了通过标准系统审计机制检测该软件的方法：

Windows：来自标准审核日志和 Sysmon 事件的事件

Linux：系统调用审计事件和 Sysmon 事件

网络审计：一套协议以及软件生成的流量

检测部分列出了软件在系统中留下的主要痕迹：命令行、可能的启动参数、它创建的服务和计划任务的名称，以及关键的网络痕迹。

克劳德剖面图中的检测部分

2025年12月，卡巴斯基威胁情报门户网站新增了Hunt Hub (HH)，这是一个检测规则库。这是卡巴斯基独有的项目，我们开发它是为了响应日益增长的市场需求。

此功能位于门户网站的“威胁形势”部分。它使分析人员能够访问检测规则：规则描述及其与 MITRE ATT&CK 战术和技术的映射关系，以及相关的网络犯罪集团。

在 Hunt Hub 部分，客户可以探索 EDR、NDR 和 NGFW 产品的检测逻辑，包括检测 AI 代理的逻辑。

Hunt Hub 存储库中的规则

规则卡规定：

详细描述

规则的严重级别、置信级别和警报格式

对可能出现的假阳性结果的描述

该规则可在以下平台上运行

规则涵盖的战术、技术和子技术。

使用规则所涵盖的技术的团体

检测逻辑显示了究竟是什么触发了该规则。

Hunt Hub 中的检测规则卡

技能是人工智能代理生态系统的关键组成部分。它们是预先编写的自然语言指令集，用于扩展代理的功能。我们之前讨论过恶意技能对代理用户构成的风险。技能可能需要使用额外的程序和服务，附带辅助脚本（例如，用 Python 编写的脚本），或者在指令文本中嵌入恶意命令。这意味着，向原本无害的人工智能代理添加技能会为恶意代码进入系统开辟多种新途径。为了防止这种情况发生，必须对从外部来源获取的任何技能进行扫描。卡巴斯基扫描引擎非常适合这项任务。它是一款网络威胁检测和响应解决方案，可与各种应用程序无缝集成。它可以检测代理技能、相关脚本以及运行中的代理生成的文件中的恶意代码。卡巴斯基扫描引擎可以检测恶意代理技能。训练好的逻辑逻辑模型 (LLM) 有多种格式。其中一些格式（例如 PTH、PT、PKL、KERS 等）还会存储参数和权重（训练数据集）。这些文件也可能包含恶意代码，因此在运行和使用它们之前进行扫描至关重要。卡巴斯基扫描引擎会扫描潜在危险的 LLM 格式文件，以查找受感染的数据。它可以反序列化和分析模型，从而检测恶意代码，而无需考虑模型大小。卡巴斯基扫描引擎可检测包含恶意代码的 LLM 文件。

卡巴斯基扫描引擎中恶意代理技能的检测

卡巴斯基扫描引擎检测到含有恶意代码的LLM

人工智能代理正迅速融入企业环境：它们帮助员工提高工作效率并承担更多任务，但也为攻击者提供了新的机会。为了在不损害安全性的前提下利用这项技术，企业需要构建多层防护并最大限度地降低风险。卡巴斯基的一系列解决方案可以为此提供帮助：

Kaspersky TIP 让您随时了解攻击者使用 AI 代理的最新情况，其 Hunt Hub 部分提供了检测可疑 AI 代理活动的规则。

卡巴斯基EDR可检测基础设施中的AI代理，并监控其在终端上的活动。如果检测到潜在的危险行为，该产品可以记录事件、通知安全运营中心（SOC），并采取响应措施，例如停止进程或隔离主机。

卡巴斯基产品中的应用控制功能可以通过应用分类全面限制企业内部人工智能代理的使用。Web 控制组件可以限制对 AIaaS 网络资源的访问。

卡巴斯基扫描引擎可以集成到各种公司流程中，以检测任何文件中的恶意代码，包括 AI 代理技能和 LLM。

当企业将上述工具综合运用时，他们就能以明智的方式采用人工智能代理：了解相关的风险以及有效降低风险的方法。

主动防护

亚历山大·基里琴科

安全部署人工智能代理：卡巴斯基产品如何缓解人工智能威胁

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