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Top AI Coding Tools in 2025: Best Models for Developers & Programmers

Top AI Coding Tools in 2025: Best Models for Developers & Programmers
Hostman Team
Technical writer
Infrastructure

In the early days of computing, programmers wrote code entirely on their own, from scratch and solo. Hardcore mode! The only help they had was paper reference books describing the syntax of specific languages.

Today, things are very different. In addition to countless electronic manuals, guides, articles, videos, and forums, we now have neural networks, arguably one of the most groundbreaking technologies of the early 21st century.

Trained on massive datasets, these AI models have become the primary source of coding assistance.

The advantages are obvious. AI coding tools speed up the development process by taking on much of the routine work involved in writing code. This allows developers to focus on architecture and logic instead of syntax errors and inefficient constructs.

Some tools generate code from scratch, and others analyze and complete already-written code.

However, in recent years, so many AI-powered projects have emerged that it can be difficult for the average person to figure out which AI is actually the best for programming.

There are both specialized and general-purpose models. Some only generate specific types of data (like code), while others handle all kinds (text, code, images). Some are free, others paid.

To determine which AI is the best for programming (and why), we first need to create a list of the top coding AIs, and then analyze the pros and cons of each one.

1. GitHub Copilot

Copilot is arguably the best AI coding assistant, developed by GitHub in collaboration with OpenAI. It’s positioned as an AI co-programmer trained on millions of open-source GitHub repositories.

Features

Developed by the largest cloud-based code hosting platform, Copilot leads the list of neural networks for programming, offering a wide range of capabilities:

  • Code Generation: Produces ready-to-use code snippets in all major languages based on text descriptions: scripts, functions, classes, even entire files. While the AI sometimes generates imperfect results, this can be resolved by making the user’s request more specific.

  • Code Translation: Converts code written in one programming language into logically equivalent code in another. This feature alone puts Copilot ahead of many other coding AIs, as not all models can do this effectively.

  • Code Autocompletion: Suggests autocompletion based on the overall context of the codebase.

  • Refactoring: Enhances code structure, optimizes algorithms, and fixes errors. It can also suggest alternative, more efficient solutions that a developer might not have initially considered.

  • Editor Integration: Integrates via plugins into popular text editors and IDEs like Visual Studio Code, Neovim, JetBrains IDEs, and others.

These features help automate routine coding tasks.

A lesser-known use case of Copilot is learning programming languages. The AI can generate simple code snippets that demonstrate the syntax and mechanics of a specific language.

Interestingly, this teaching method aligns with Stephen Krashen’s Comprehensible Input Hypothesis, which states that language acquisition is driven by understandable input, i.e., the material that the learner can interpret with explanation.

Similarly, Copilot can be used as an interactive reference, potentially replacing resources like Stack Overflow.

Languages Supported

Copilot supports all major programming languages: C, C++, C#, Go, Java, JavaScript, Kotlin, PHP, Python, Ruby, Rust, Scala, Swift, and TypeScript.

It can also generate code using popular frameworks and libraries like React, Angular, Vue.js, Node.js, Django, Flask, and Ruby on Rails.

Pricing Plans

Naturally, GitHub offers only a limited set of Copilot features for free. The free version also has monthly limits on code generations.

The full version is available through subscriptions for individuals, teams, and enterprises. Pricing starts at $4/month, with a 30-day free trial. In return, users get a powerful tool for faster coding.

Despite requiring a subscription, many developers consider Copilot the best AI coding assistant, especially when compared to general-purpose models like ChatGPT, which aren't primarily designed for code generation.

2. Tabnine

Tabnine is an AI that generates code snippets not based on explicit prompts but on the development context formed by the programmer’s current work.

Features

Unlike Copilot, Tabnine primarily focuses on code autocompletion. However, it also offers several distinctive features:

  • Offline Mode: The Enterprise version of Tabnine can run entirely offline, generating code without internet access. This improves data privacy, as code is processed locally and not sent to the cloud; however, it does require more system resources.

  • Personalized Generation: Tabnine learns from a specific developer’s codebase, mimicking their unique style and preferences. This results in personalized suggestions that feel as if the code were written by the developers themselves, in contrast to Copilot, which was trained on public GitHub repositories.

  • IDE Integration: Since Tabnine is not a standalone application but a smart autocompletion engine, it integrates with virtually all major IDEs through plugins, including VS Code, IntelliJ, Visual Studio, Eclipse, Android Studio, AppCode, CLion, GoLand, Neovim, PhpStorm, PyCharm, Rider, RubyMine, WebStorm.

  • Interactive AI Chat: Tabnine also offers a built-in chat interface for personalized communication with the AI. Users can ask questions related to the code in their current editor tab.

All in all, Tabnine is geared more toward typing speed and efficiency rather than generating large chunks of code from scratch. Think of it as classic autocompletion but supercharged with AI.

Languages Supported

Like Copilot, Tabnine supports autocompletion for all major programming languages: Python, JavaScript, TypeScript, Java, C/C++, C#, Go, Ruby, Swift, PHP, Rust, Kotlin, Perl, Dart, Scala.

Pricing Plans

Tabnine is available under two subscription plans:

  • Dev – from $9/month for individual developers.
  • Enterprise – from $39/month for teams and companies.

The Enterprise plan offers advanced customization options, enhanced security, and on-premise deployment for maximum privacy.

3. ChatGPT

ChatGPT is a generative AI developed by OpenAI, based on the GPT (Generative Pre-trained Transformer) architecture.

Unlike Copilot and Tabnine, ChatGPT can generate not just code but also various forms of text content. That makes it a general-purpose neural network—a versatile tool for generating any data based on a user's prompt.

Some might argue that ChatGPT is more text-oriented than code-focused. However, it remains one of the best free AI for coding, as its basic version is available to everyone without a paid subscription.

Features

ChatGPT operates through a chat interface, where prompts are entered as natural language messages.

That’s why its name consists of Chat and GPT:

  • Chat: its ability to hold conversations, answer questions, and maintain real-time dialogue.
  • GPT: short for Generative Pre-trained Transformer:
    • Generative: creates original text rather than copying answers,
    • Pre-trained: trained on vast data corpora,
    • Transformer: refers to the model’s architecture, which uses attention mechanisms to understand and generate contextually accurate responses.

In short, ChatGPT is a conversational AI capable of tackling almost any language-based task — including code generation.

Here’s what it can do:

  • Conversational Interaction: The AI responds as if you were chatting with another person. You can ask it to use a certain tone, generate text, tables, code, and even simple images. This makes ChatGPT a virtual assistant, coworker, or advisor.

  • Free Code Generation: The base model is completely free to use. More advanced versions offer improved performance but require a subscription.

  • Multi-Format Output: It can create more than just code. You can generate any language-based content based on a clear prompt and adapt it to the ongoing context of the conversation.

For example, you could write this fun prompt:

“Give me an example of Python code with a helicopter and a car class. Each should have a fuel variable initialized to 100. Then create objects of each class and have the helicopter hook the car with a cable.”

ChatGPT would generate something like this:

class Helicopter:
    def __init__(self):
        self.fuel = 100
        self.hooked_car = None

    def hook_car(self, car):
        self.hooked_car = car
        print("The helicopter has hooked the car with a cable.")

class Car:
    def __init__(self):
        self.fuel = 100

helicopter = Helicopter()
car = Car()
helicopter.hook_car(car)

You can check this code in any online Python interpreter and get the expected output:

The helicopter has hooked the car with a cable.

So, if you're working late at night and wondering which neural network is best for hands-off code generation, ChatGPT is worth considering. After all, OpenAI is a global leader in machine learning.

At the very least, ChatGPT is the best conversational AI for code creation, capable of generating not only code but also full documents, tables, and even basic images.

Languages Supported

Since it was trained on a vast linguistic dataset, ChatGPT can generate code in nearly any language and not just general-purpose ones.

It supports all major programming languages, including Python, JavaScript, TypeScript, Java, C, C++, C#, Go, PHP, Swift, Kotlin, Ruby, Rust, Haskell, Lisp, Elixir, Erlang, and F#.

It also understands domain-specific languages: HTML, CSS, SASS/SCSS, SQL, GraphQL, Shell, PowerShell, Lua, Perl, YAML, and JSON.

Listing them all would be pointless, as ChatGPT can understand and generate code or text in virtually any format. That's its defining strength.

Pricing Plans

OpenAI offers four subscription tiers for ChatGPT, each expanding the capabilities of the last:

  • Free – All basic features. No cost.
  • Plus – Enhanced performance and access to newer models with better contextual understanding and faster responses. Starts at $20/month.
  • Pro – Full access with no usage limits. Starts at $200/month.
  • Team – Adds collaborative tools, custom roles, and enhanced security for data sharing and storage. Team data is excluded from AI training — ensuring full confidentiality. Starts at $25/month.

Paid plans provide higher accuracy, better performance, and more stability. Still, the free version offers nearly identical functionality — the difference lies in the fine details.

4. Claude

Claude is another natural language processing AI developed by Anthropic. According to its creators, Claude is a safer, more ethical, and more predictable alternative to ChatGPT.

Features

Overall, Claude's capabilities are similar to ChatGPT’s, with a few notable distinctions:

  • Image and Document Analysis: Claude can interpret the contents of images and documents in detail, recognizing real-world objects, diagrams, graphs, numbers, and text. ChatGPT is also capable of this, but only in its paid version. Claude offers it natively.

  • Massive Context Window: Claude supports up to 200,000 tokens, which allows it to analyze large volumes of data. By comparison, ChatGPT maxes out at around 128,000 tokens. One token is roughly 5 characters of English text.

  • High Ethical Standards: Thanks to built-in ethical constraints, Claude is less likely to generate inappropriate content, making its responses more conservative. While this may not matter to some users, from a broader perspective, output filtering is a key trait that separates the best AI coding tools from the rest, especially as AI tools become mainstream.

In short, Claude offers high factual accuracy, which is crucial for generating reliable code based on user instructions.

Languages Supported

According to Anthropic, Claude performs best when generating Python code. However, it also supports other popular languages: JavaScript, Java, C++, Go, PHP, Ruby, C#, Swift, TypeScript, Kotlin, and Rust.

Of course, the full list of supported languages isn’t publicly available, as the model was trained on diverse datasets. Practical testing is the best way to determine support.

Pricing Plans

Claude offers several pricing tiers:

  • Free – Standard chat access via browser or mobile app (iOS/Android). No cost.
  • Pro – Enables structured chats, document analysis, and access to additional Claude models and features. Starts at $18/month.
  • Team – Adds collaboration features for group work. Starts at $25/month.
  • Enterprise – Provides deeper control over generation processes, user role management, and enhanced data privacy. Custom pricing.

Despite Claude being one of the top free AI for coding, it can’t be considered a full competitor to ChatGPT.

Here’s why:

  • Smaller Knowledge Base: ChatGPT was trained on more data, producing more accurate and diverse responses.
  • Limited Availability: Claude is not as widely accessible as ChatGPT and is available in fewer countries.
  • Few Integrations: ChatGPT is integrated into many products (e.g., Office, Azure), while Claude is not.
  • Slower Development: ChatGPT evolves rapidly, releasing updates and features faster than Claude.

Still, Claude is worth trying for anyone who regularly uses AI in programming or text generation tasks.

5. Snyk Code

Snyk Code is an AI-powered static analysis tool for detecting vulnerabilities and errors, part of the broader Snyk ecosystem.

Features

Trained on a database of known vulnerabilities (updated regularly), Snyk Code focuses on secure development:

  • Vulnerability Detection: Performs real-time code analysis during development and commits to catch threats before they reach production.

  • Development Tool Integration: Works with GitHub, GitLab, Bitbucket, and Azure Repos, and is compatible with popular IDEs: VS Code, IntelliJ IDEA, PyCharm, WebStorm, Eclipse.

  • Contextual Fix Recommendations: For every issue found, it provides an explanation and sample fixes, helping developers patch their code quickly and securely.

In essence, Snyk Code is best used after you have written the code as an added security layer before deployment.

Languages Supported

Snyk Code supports major programming languages only: Apex, C, C++, Go, Groovy, Java, Kotlin, JavaScript, .NET, PHP, Python, Ruby, Scala, Swift, Objective-C, TypeScript, VB.NET.

Pricing Plans

Snyk Code is free for individual use, but teams and companies can choose from the following:

  • Free – Basic analysis with a limit of up to 200 scans per month.
  • Team – Adds support for private repos, CI/CD integration, and advanced security features. Starts at $25/month.
  • Enterprise – Includes local deployment, advanced analytics, and enterprise-level controls. Custom pricing.

While Snyk Code doesn’t generate code, its powerful analysis tools and free tier perfectly justify its inclusion in any list of the best free AI tools for coding.

6. Documatic

Documatic is an AI that automatically generates documentation and enables codebase exploration. It analyzes the project, extracts key information, and structures it for easy reference.

Features

Documatic is designed for codebase analysis; all other functionality stems from this core:

  • Automatic Documentation Generation: Produces detailed code explanations, reducing the need for manual comments.

  • Code Search and Navigation: Responds to developer queries with relevant code snippets and context.

  • Project Structure Visualization: Displays project components (dependencies, microservices, repos) as interactive graph nodes, useful for understanding complex architectures.

  • Code Explanation: Clarifies algorithms and logic, making unfamiliar projects easier to understand.

Documatic is passive: it doesn’t generate code, only analyzes and documents it.

Languages Supported

It supports modern interpreted and compiled languages: Python, Java, JavaScript, TypeScript, Go, C#, PHP.

Pricing Plans

Documatic keeps things simple with just two tiers:

  • Free – Full basic functionality for solo developers, including auto-documentation. No cost.
  • Team / Enterprise – A combined tier for corporate use, offering unlimited analysis, integrations, reporting, and collaboration tools. Custom pricing.

While it’s easy to chase the best AI coding tools, it’s crucial to remember: the developer matters more than the AI. Skills, logic, creativity, and experience outweigh any neural network’s output.

You should only upgrade to premium tools when free features no longer meet your needs.

7. Mintlify

Mintlify is a comprehensive online platform for automating code documentation with AI.

Unlike Documatic, Mintlify offers cloud hosting with visually styled, user-accessible documentation sites.

For instance, a developer or team building a JavaScript library can generate full documentation from a GitHub repo, resulting in a live, multi-page site with API references. These pages are editable using a WYSIWYG editor.

Fun fact: Anthropic uses Mintlify to power the documentation for Claude.

Features

Mintlify connects the project’s codebase to a public-facing documentation site, offering:

  • Automated Documentation Generation: Generates detailed documentation (including API references) directly from your codebase.

  • Version Control Integration: Syncs with GitHub and GitLab, ensuring documentation updates automatically when the code changes, which makes it perfect for CI/CD pipelines.

  • Documentation Site Hosting: Creates a stylish, SEO-optimized site with editable sections.

  • Analytics & Feedback: Provides user analytics and supports direct feedback collection to improve documentation quality.

While powerful, Mintlify has a learning curve as its feature-rich interface takes time to master.

Languages Supported

Supports 12 modern languages: Python, JavaScript, TypeScript, C, C++, PHP, Java, C#, Ruby, Rust, Dart, Go.

Pricing Plans

Mintlify offers four plans:

  • Hobby – Free plan for individuals with full standard functionality.
  • Pro – Advanced configuration and analytics. Starts at $150/month.
  • Growth – Allows full customization, branding removal, and other perks. Starts at $550/month.
  • Enterprise – Full configuration control and dedicated support. Custom pricing.

Where other AI coding tools show their intelligence directly, Mintlify’s AI works silently in the background.

At first glance, it may seem like a manual documentation editor; however, over time, it reveals itself as an automation powerhouse, seamlessly connecting code to documentation.

8. Codeium

Codeium is an AI-powered coding assistant that consists of several products built on artificial intelligence:

  • Windsurf Editor – an integrated development environment (IDE) with built-in AI.
  • Forge – an AI assistant for code analysis and review.

In addition to these, there’s a browser-based chat called Live, as well as numerous IDE extensions – Codeium Extensions.

E7261d0c 08e8 4178 9ec8 Bb509066df1d.png

The Codeium Windsurf Editor integrated development environment, with the code editor on the left and the AI chat on the right. Source: codioailab.com

Features

Codeium offers a wide range of features that assist during coding and code editing:

  • Code Autocompletion: Provides intelligent suggestions as you type.

  • Chat Assistant: A built-in AI chat can explain code snippets in detail, offer refactoring suggestions (passively while you write), and answer programming questions directly within the development environment. It can also advise on build commands and configuration.

  • Intelligent Search: Ensures quick access to classes, methods, functions, and code fragments, streamlining navigation in large codebases.

Essentially, Codeium aims to provide a comprehensive suite of tools for virtually all coding scenarios – all powered by AI.

Languages Supported

Supports all popular programming languages, including: Python, JavaScript, TypeScript, Go, Java, C#, PHP, Ruby, Kotlin, Swift.

Pricing Plans

Codeium offers several pricing plans for both individual developers and entire teams:

  • Free – All standard features. Free of charge.
  • Pro – Expanded context and deeper AI understanding, faster autocompletion, and other advanced features. Starting at $15/month.
  • Pro Ultimate – Even more useful tools and priority support. Starting at $60/month.
  • Teams – Collaboration and analytics tools for teams. Starting at $35/month.
  • Teams Ultimate – Enhanced AI model access. Starting at $90/month.
  • Enterprise SaaS – Custom pricing upon request.

9. Gemini

Gemini is a versatile AI developed by Google. Despite being relatively new, it rounds out our list of the top AI coding assistants in 2025. Unsurprisingly, it’s a direct competitor to both ChatGPT and Claude. 

Features

It’s important to recognize that Google is a major player (arguably a monopolist) in the software market. With vast cloud infrastructure, massive data resources, and many popular services (plus its own OS, Android), Gemini offers a broad array of capabilities for working with both text and visual data:

  • Text Generation, Analysis, and Translation.

  • Image Generation and Analysis: Generates images from text prompts and can also analyze images and describe their contents.

  • Code Generation and Analysis: Generates code snippets in any language and format. Also understands and analyzes code, providing suggestions for improvement. Google also offers the Gemini Code Assist extension for popular IDEs.

  • Integration with Google Services: Integrated with many Google apps and Android tools.

  • Fast Response Generation: Provides answers faster than ChatGPT and generally operates at a higher speed.

  • Large Context Window: Can handle up to 1 million tokens.

Notably, the advanced capabilities of Gemini’s language model are available through a special AI Studio for developers. This environment allows not only text-based interaction but also screen sharing for more detailed feedback.

AI Studio is designed for app developers who want to test Gemini integration with their products.

Languages Supported

Gemini supports the following major programming languages: Python, Java, C++, JavaScript, Go, TypeScript, C#, Ruby, PHP, Swift, Kotlin, Rust, SQL, HTML, CSS, Bash, Perl, Lua, R, Dart, Scala, Julia, Fortran.

Pricing Plans

Google offers a fairly straightforward pricing structure for Gemini:

  • Free – Standard model access.
  • Advanced – Enhanced performance, accuracy, and multimodal capabilities. Starting at $22/month.

Thus, just like ChatGPT, Gemini is another great free AI for programming, particularly when it comes to working with general-purpose data. The ability to generate not only code but also supporting text is an important asset in development.

Conclusion

So, what is the best AI for coding? That’s for each user to decide. Some may be satisfied with intelligent autocompletion, while others may require the generation of large code fragments across multiple languages – complete with detailed explanations.

Model

Type

Features

Pricing

Copilot

Specialized

Code generation, autocompletion

Subscription

Tabnine

Specialized

Autocompletion

Subscription

ChatGPT

General

Generation, analysis

Free, subscription

Claude

General

Generation, analysis

Free, subscription

Snyk Code

Specialized

Analysis

Free, subscription

Documatic

Specialized

Documentation

Free, subscription

Mintlify

Specialized

Documentation, hosting

Free, subscription

Codeium

Specialized

Generation, analysis

Free, subscription

Gemini

General

Generation, analysis

Free, subscription

Ultimately, the most important factor is not the tool itself, but the developer using it. Skills, experience, logic, critical thinking, and creativity all outweigh the capabilities of any neural network.

So, switching to paid versions of AI products – whether they’re code generators or analyzers – only makes sense when the free version clearly falls short for your needs.

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Oracle VM VirtualBox Oracle VM VirtualBox is a cross-platform hypervisor for Windows, Linux, macOS, and other systems.  It is one of the most popular hypervisors, especially in the mass market segment. This is partly because VM VirtualBox has open source code. The program is distributed under the GNU GPL license. A distinctive feature: VirtualBox offers broad compatibility across different host and guest operating system combinations, making it ideal for development and testing environments. Hypervisors vs. Containers Hypervisors are often contrasted with containers. They allow deploying a greater number of applications on a single device. You already know what a hypervisor is and how it works. The problem is that VMs consume many resources. To operate, you need to make a copy of the operating system, plus a complete copy of the equipment for this system to function. If you allocate a nominal 4 GB of RAM to a VM, then the main device will have 4 GB less RAM. Unlike VMs, a container only uses the operating system's resources. It also needs power to run a specific application. But much less is required than to run an entire OS. However, containers cannot completely replace VMs. This is partly due to the increased risk of losing all data. Containers are located inside the operating system. If the host is attacked, all containers can be damaged or lost. A virtualization server creates multiple virtual machines. They don't interact with each other; there are clear boundaries between them. If one machine is attacked, the others remain safe. Along with all their contents. In modern infrastructure, containers and VMs are often used together. Container orchestration platforms like Kubernetes typically run on virtual machines, combining the isolation benefits of VMs with the efficiency of containers. This hybrid approach has become the standard for cloud-native applications. Security Issues Hypervisors are more secure than containers. However, they still have problems. Theoretically, it's possible to create a rootkit and malicious application that installs itself disguised as a hypervisor. Such a hack is called hyperjacking. It's difficult to detect. Protection doesn't trigger because the malicious software is already installed and intercepts system actions. The system continues to work, and the user doesn't even suspect there are problems. To protect the system from rootkits, specialists are developing various approaches that protect it without negatively affecting performance. Modern processors include hardware-based security features like Intel TXT and AMD Secure Encrypted Virtualization to help prevent hypervisor-level attacks. How to Choose a Hypervisor The choice is vast: VMware or VirtualBox, Hyper-V or KVM. There's one universal recommendation: focus on the tasks. If you need to test an operating system in a virtual machine on a home computer, VirtualBox will suffice. If you're looking for a solution to organize a corporate-level server network, then the focus shifts toward VMware tools (keeping in mind recent licensing changes), KVM-based solutions like Proxmox, or cloud-native options. For cloud deployments, consider managed hypervisor solutions from providers like Hostman, AWS, Azure, or Google Cloud, which abstract away much of the complexity while providing enterprise-grade performance and security.
20 October 2025 · 9 min to read
Infrastructure

Information Security (InfoSec): Definition, Principles Triad, and Threats

Information security refers to various methods of protecting information from outsiders. That is, from everyone who should not have access to it. For example, a marketer typically has no reason to view the company's financial statements, and an accountant doesn't need to see internal documents from the development department. Before the era of universal digitization, it was mainly paper documents that needed protection. They were hidden in safes, secret messages were encrypted, and information was transmitted through trusted people. Today, computer security is the foundation of any business. InfoSec Principles Information security protection is based on three principles: availability, integrity, and confidentiality. Confidentiality: data is received only by those who have the right to it. For example, application mockups are stored in Figma, with access limited to designers and the product manager. Integrity: data is stored in full and is not changed without permission from authorized persons. Suppose there's code in a private repository. If an unauthorized person gains access to the repository and deletes part of the project, this violates integrity. Availability: if an employee has the right to access information, they receive it. For example, every employee can access their email. But if the email service is attacked and made unavailable, employees won't be able to use it. Adhering to these principles helps achieve the goal of information security: to reduce the likelihood of or eliminate unauthorized access, modification, distribution, and deletion of data.  Many companies also adopt a zero-trust security approach that assumes no user or system should be trusted by default. This reinforces all three principles by requiring continuous verification. What Information Needs Protection Understanding what data should be protected is what information security in a company depends on. Information can be publicly accessible or confidential. Publicly accessible: this data can be viewed by anyone. Confidential: available only to specific users. At first glance, it seems that information security measures don't apply to publicly accessible information, but this isn't true. Only the principle of confidentiality doesn't apply to it. Publicly accessible data must remain integral and, logically, available. For example, a user's page on a social network. It contains publicly accessible information. The social network ensures its availability and integrity. If the user hasn't changed privacy settings, anyone can view their page. But they cannot change anything on it. At the same time, the account owner can configure confidentiality, for instance, hide their friends, groups they're subscribed to, and musical interests. Confidential information also comes in different types. These can be: Personal user data. Trade secrets: information about how the company operates and what projects it conducts and how. Professional secrets, which must be kept by doctors, lawyers, notaries, and representatives of certain other professions. Official secrets: for example, pension fund data, tax inspection information, banking details. State secrets: intelligence information, data on economic conditions, foreign policy, science and technology. This is not an exhaustive list, but rather an attempt to show how much data needs information security measures applied to it. Possible Threats The enormous list of potential threats is usually divided into four types: Natural: for example, hurricanes or floods. Man-made: phenomena related to human activity. They can be unintentional (employee error) or intentional (hacker attack). Internal: threats that originate from within the system, such as from employees. External: threats that originate from other sources, such as attacks by competitors. With the mass adoption of remote work formats, the number of man-made threats, both external and internal, intentional and unintentional, has noticeably increased. Because of this, the workload on information security specialists has grown. Today's threat environment includes several increasingly prevalent attack vectors: Ransomware attacks: malicious software that encrypts company data and demands payment for its release. These attacks have become more sophisticated and targeted, often crippling entire organizations. Supply chain attacks: compromising software or hardware providers to gain access to their customers' systems. Attackers exploit trust relationships between organizations and their vendors. AI-powered threats: artificial intelligence is being used to create more convincing phishing campaigns, generate deepfakes for social engineering attacks, and automate vulnerability discovery. At the same time, AI is also being deployed defensively to detect and respond to threats faster. Social engineering and deepfakes: attackers use AI-generated audio and video to impersonate executives or trusted individuals, making fraudulent requests appear legitimate. Protection Measures Organizational information protection measures are implemented at several control levels. Administrative: the formation of standards, procedures, and protection principles. For example, developing a corporate security policy. At this level, it's important to understand what data you will protect and how. Logical: protection of access to software and information systems. At this control level, access rights are configured, passwords are set, and secure networks and firewalls are configured. Physical: at this level, physical infrastructure is controlled. This refers not only to access to equipment, but also to protection from fires, floods, and other emergency situations. Despite digitization, physical information protection remains no less important. Antivirus software and access rights separation won't help if attackers gain physical access to the server. They won't save you in case of an emergency either. To eliminate such problems, Hostman uses infrastructure in protected data centers.
20 October 2025 · 5 min to read

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