Mit dem KI-Modernisierung-Toolkit in wenigen Monaten von Legacy-Systemen zu modernen Lösungen

Mateusz Mnich

Scaling digital innovation in drug discovery and clinical trials

Boosting Production Process Efficiency with Data and AI

<div class="tableOfContent">
<div class="tableOfContentHeader">
<h2 class="tableOfContentTitle">Table of contents:</h2>
</div>
<div class="tableOfContentItem"><a href="#Smarter_data_handling">
Smarter data handling, less paperwork
</a></div>
<div class="tableOfContentItem"><a href="#A_new_era">
A new era of property intelligence
</a></div>
<div class="tableOfContentItem"><a href="#Faster_safer_smoother">
Faster, safer, smoother transactions
</a></div>
<div class="tableOfContentItem"><a href="#AI_in_real_estate">
AI in real estate – where is the industry headed?
</a></div>
<div class="tableOfContentItem"><a href="#FAQ">
FAQ section
</a></div>
</div>
<a href="https://www.pwc.com/us/en/industries/financial-services/asset-wealth-management/real-estate/emerging-trends-in-real-estate.html" target="_blank" rel="noopener">Real estate has always relied on data</a>. But nowadays it’s increasingly important to make the best use of the data you collect. This means leveraging it quicker and more wisely than your competitors. AI-driven data agents make this process easier and more efficient. The real estate industry is betting big on these tools. AI in the industry is expected to grow from <a href="https://www.thebusinessresearchcompany.com/report/ai-in-real-estate-global-market-report" target="_blank" rel="noopener">$300 million to close to 1 trillion in 2029</a>, indicating AI investment isn’t slowing down anytime soon and <a href="https://softwaremind.com/services/real-estate-software-development-consulting-services/">real estate software development</a> will be crucial for businesses.

These tools speed up processes greatly. Not only do they make property valuation effortless, but AI-controlled market analytics, chatbots and virtual assistants improve communication with clients and help agents understand listings better, avoiding frequently overlooked details. They also give clients more confidence when making the decision to close on a property. However impressive, these tools aren't meant to replace people. It’s about giving them more accurate information so that it's less risky to act on their instincts.
<h2 id="Smarter_data_handling">Smarter data handling, less paperwork</h2>
Real estate professionals deal with mountains of paperwork every day. They are required to go through appraisals, mortgage applications, inspection reports and various other contracts before a property can be sold. Traditionally, processing these took hours of manual labor, not to mention the risk of human error was high.

According to McKinsey, “AI-driven market analysis tools can <a href="https://artsmart.ai/blog/ai-in-real-estate-statistics/" target="_blank" rel="noopener">identify emerging real estate trends with 90% accuracy</a>, aiding in strategic decision‑making.” For the first time ever, we have AI agents that can sort through data in seconds and determine, using predictive analytics and valuation tools to determine which data is the most relevant. If an agent wishes to check a mortgage clause or research old appraisals, they can do this with one click. Through the use of AI and automation, agents can spend less time buried in files and more time helping their clients.

Looking for a real-world example? Look no further than the mortgage industry. Companies like <a href="https://blend.com/products/mortgage-suite/" target="_blank" rel="noopener">Blend and Better.com</a> utilize AI agents to pre-fill loan documents. They flag inconsistencies and expedite approvals. According to the Federal Reserve Bank of New York, “Across specifications, FinTech lenders (which utilize digital tools, automation, and AI-driven technology) process mortgages <a href="https://www.newyorkfed.org/medialibrary/media/research/staff_reports/sr836.pd" target="_blank" rel="noopener">9.3 to 14.6 days faster than other lenders</a>.” In the future, the widespread adoption of AI will likely mean reducing processing times further, from days to minutes. But AI’s reach goes further than home loans. Inspections, leases and zoning docs can all benefit by catching problems early, before they become major issues.

Read more: <a href="https://softwaremind.com/blog/how-ai-is-changing-the-real-estate-market/">AI for Real Estate</a>
<h2 id="A_new_era">A new era of property intelligence</h2>
Buyers are no longer satisfied with taking a walk through a potential new property. They want, if not demand, the whole story, i.e., the walking distance to the market and the reputation of the local school. They also want to know what a property could become, and what its resale value might be. Every angle matters.

AI removes guesswork. An experienced agent has always done more than show homes. They build up their knowledge around an area and determine whether it is predicted to grow or decline. They know when a selling price is too high or below market value. With their knowledge, they can shape strategies that match the buyer’s aim and willingness to sign on the dotted line.

That’s where AI shines. Decisions can be made much faster as the agents are equipped with all the facts. Data analysis agents can sift through massive datasets – local amenities, historical prices, demographic shifts – and generate real-time valuations, tailored to their clients. AI agents don’t just crunch numbers for them. They determine and predict patterns.

For instance, platforms like Zillow and HouseCanary are already using <a href="https://www.zillow.com/z/zestimate/" target="_blank" rel="noopener">machine learning to forecast home values</a> with increasing precision. These predictive tools have incredible accuracy. Zillow’s AI-powered Zestimate valuations, for instance, now have a median error of only about 1.9%, when predicting the value of a home. This has resulted in smarter decisions, more confident investors, and fewer missed opportunities.

And it’s not just for the major players with large financial backing anymore. Buyers and sellers with smart apps in their pockets are instantly empowered. They see all the angles, dodge the traps, and cut through the noise to discover what property is really worth. If anything, AI agents are making it a fair fight now. And early adopters are coming out ahead.
<h2 id="Faster_safer_smoother">Faster, safer, smoother transactions</h2>
As previously mentioned, closing a real estate deal can feel like walking blind through a maze. There are documents to verify, signatures to track down, too many moving parts. Delays pile up fast.

AI clears the way. AI agents scan the required papers and keep the deal moving. And with other AI-powered security measures, the whole process becomes cleaner and more secure – built on a higher level of trust than ever before, as already witnessed by real estate firms like Propy and Ubitquity that have <a href="https://propy.com/home/" target="_blank" rel="noopener">pioneered blockchain-backed property transfers</a>. With every transaction recorded on a blockchain ledger, there’s a permanent, tamper-proof trail. No more digging through cabinets or questioning a deal’s history. Everything is visible and verifiable.

This shift to precision ensures that contracts never miss a deadline. Moreover, buyers no longer must worry about what is hidden in the fine print, or how it might be interpreted, as their AI agent is always there to clarify.
<h2 id="AI_in_real_estate">AI in real estate – where is the industry headed?</h2>
Using AI for data isn’t just staying current. It’s about securing your company’s future. AI agents are fast becoming vital tools in a hard-fought market. Those who adapt early will have a greater chance of success. Morgan Stanley Research reports that <a href="https://www.morganstanley.com/insights/articles/ai-in-real-estate-2025/" target="_blank" rel="noopener">AI can automate up to 37% of tasks in real estate,</a> unlocking an estimated $34 billion in efficiency gains by 2030. The firms using these tools now run leaner, have improved client relations and scale faster. Whether you’re managing buildings, backing loans, or helping buyers, these tools can give you a much-needed edge.

What does all this mean? Essentially, adaptation is no longer a luxury. It’s survival. In industries where one bad bet can cost millions, seeing the trend before others do can make all the difference.  Firms that cling to old methods will be left behind – slower, riskier, blind to what is coming. But those who harness AI’s reach, those who harness the data will outlast and outpace the rest.

At Software Mind, we know this is more than a trend. It’s the new foundation of real estate.  Reach out to see how our data tools can help your real estate business grow – <a href="https://softwaremind.com/contact/">get in touch using this form</a>.
<h2 id="FAQ">FAQ</h2>
<h3>What are data analysis agents in real estate?</h3>
Data analysis agents are advanced AI-driven tools designed to analyze vast amounts of real estate data. They assist professionals in making faster, more informed and more accurate decisions. These agents can assess property valuations, detect emerging trends and support various workflows, such as client profiling, lead prioritization and compliance checks.
<h3>How do data agents reduce paperwork and manual tasks?</h3>
AI-powered agents automate many of the routine, time-consuming administrative tasks that real estate professionals typically handle. This includes scanning and verifying mortgage documents, inspection reports, contracts and appraisals. They can auto-fill forms, flag discrepancies and track document statuses – significantly cutting down on manual effort, reducing processing time and minimizing human error.
<h3>Will AI eventually replace real estate agents or brokers?</h3>
No. AI is meant to enhance, not eliminate, the role of real estate professionals. These tools provide valuable insights, automate repetitive tasks, and streamline operations - freeing agents to focus on what they do best: building trust, managing relationships, negotiating deals and delivering strategic advice based on human understanding and context.
<h3>How does AI help make property transactions faster and more secure?</h3>
AI agents accelerate the entire transaction process by automating document verification, tracking key milestones and alerting stakeholders to any missing or overdue steps. When combined with technologies like blockchain, the entire process gains a new layer of security and transparency - ensuring all data is traceable, tamper-proof and easy to audit. This helps reduce delays, minimize risks and increase trust between all parties.
<h3>What insights can AI tools provide to homebuyers and property sellers?</h3>
AI tools give both buyers and sellers a comprehensive, data-driven view of the market. This includes analysis of neighborhood features, local crime rates, school district quality, historical price trends, expected appreciation or depreciation and property comparables. These insights empower users to make confident, evidence-based decisions instead of relying purely on intuition or outdated data.

How Data Analysis Agents are Revolutionizing Real Estate in 2025

<div class="tableOfContent">
<div class="tableOfContentHeader">
<h2 class="tableOfContentTitle">Table of contents:</h2>
</div>
<div class="tableOfContentItem"><a href="#What_is_Model_Context_Protocol">
What is Model Context Protocol (MCP)?
</a></div>
<div class="tableOfContentItem"><a href="#MCP_server_as_integration_layers">
MCP server as integration layers?
</a></div>
<div class="tableOfContentItem"><a href="#Why_MCP_matters_for_enterprises">
Why MCP matters for enterprises
</a></div>
<div class="tableOfContentItem"><a href="#Security_and_access_control_in_MCP_integrations">
Security and access control in MCP integrations
</a></div>
<div class="tableOfContentItem"><a href="#MCP_severs_integration">
MCP severs integration – an AI-powered platform shift for enterprises
</a></div>
<div class="tableOfContentItem"><a href="#MCP_servers_evolution">
MCP servers – the next stage of AI evolution
</a></div>
</div>
Model Context Protocol (MCP) servers are rapidly becoming the backbone of a new era of collaborative AI, by delivering an integration layer that enables seamless communication and cooperation between diverse and specialized <a href="https://softwaremind.com/services/generative-ai-development-services/">artificial intelligence agents</a>.

According to <a href="https://www.gartner.com/en/documents/6403275" target="_blank" rel="noopener nofollow">Gartner</a>, MCPs are expected to become a widely adopted standard for API gateway vendors. Specifically, 50% of Integration Platform as a Service (iPaaS) vendors will adopt MCPs by 2026. During the <a href="https://blogs.microsoft.com/blog/2025/05/19/microsoft-build-2025-the-age-of-ai-agents-and-building-the-open-agentic-web/" target="_blank" rel="noopener nofollow">Build 2025</a> conference, Microsoft emphasized that the future of AI agents is directly linked to open standards and shared infrastructure, which will enable unique capabilities for customers. The company aims to provide extensive first-party support for MCP across its agent platforms and frameworks, including GitHub, Copilot Studio, Dynamics 365, Azure AI Foundry, Semantic Kernel and Windows 11.

This article will explain what the MCP is, how MCP servers operate, their significance and the advantages MCP integration brings when implemented in enterprises by an experienced team.
<h2 id="What_is_Model_Context_Protocol">What is Model Context Protocol (MCP)?</h2>
Originally designed as a standardized method for individual AI models to interact with various tools and data sources, the MCP standard – introduced by <a href="https://www.anthropic.com/news/model-context-protocol" target="_blank" rel="noopener nofollow">Anthropic</a> in November 2024 – has evolved into a fundamental technology for multi-agent systems. The MCP is an open standard that allows developers to create secure, two-way connections between their data sources and AI-powered tools. Sometimes called USB-C for AI tools, it enables plug-and-play combability, by making it possible to develop a tool that can be used by any agent. The architecture is simple: developers can either share their data through MCP servers or create AI applications (MCP clients) that connect to these servers. However, to truly build AI systems that deliver business value, enterprises first need an integration layer that lets AI safely tap into existing systems and data. MCP servers deliver just that.
<h2 id="MCP_server_as_integration_layers">MCP server as integration layers?</h2>
Lightweight programs or services that act as an adapter for a specific tool or data source, and MCP server exposes certain functionalities of that tool in a standardized manner. Instead of requiring the AI to understand the details of a specific API, such as Salesforce, or a SQL database, the MCP server informs the AI about the "tools" it offers – for example, looking up a customer by email or a query to retrieve today's sales total. It works like a contract: the MCP server defines what it can do in a machine-readable format and how to call its functions. The AI model can read this contract and comprehend the available actions.

At its core, the MCP follows a client–server architecture. On one side is the MCP client (built into the AI application or agent), and on the other side are one or more MCP servers (each connecting to a specific system or resource). The AI-powered app – for example, an AI assistant like Claude or ChatGPT, or a smart (integrated development environment) IDE – acts as the MCP host that can connect to multiple servers in parallel. Each MCP server might interface with a different target: one could connect to a cloud service via its API, another to a local database, another to an on-premise legacy system. Crucially, all communication between the AI (host) and the servers follows the standardized MCP protocol, which uses structured messages to format requests and results consistently.

One significant feature of the MCP is that it changes the integration model. Instead of hard-coding an AI to use a specific API, the system informs the AI about its actions and how to perform them. The MCP server essentially communicates, “Here are the functions you can call and the data you can access, along with descriptions of each.” This allows the AI agent to discover these functions at runtime and invoke them as needed, even combining multiple tool calls to achieve a goal. In essence, MCP decouples the AI from being tied to any particular backend system. As long as a tool has an MCP server, any MCP-enabled AI can utilize it.
<h2 id="Why_MCP_matters_for_enterprises">Why MCP matters for enterprises</h2>
By allowing companies to take advantage of AI solutions and accelerate their business, MCP servers will play an essential role in the enterprise context. Here's what's most crucial:

Breaking down silos with a unified standard: Enterprises often use a combination of legacy systems, modern cloud applications and proprietary databases. MCP simplifies this landscape by replacing numerous individual integrations with a single standard protocol. This enables AI systems to access data from all these sources in a consistent manner. As a result, redundant integration efforts are eliminated, and developers only need to create or adopt an MCP connector once. In this way, any AI agent can utilize it without reinventing the wheel for each new model or tool.

Making AI agents useful: By giving AI real hooks into business systems, MCP turns AI from a passive Q&amp;A assistant into an active problem-solver. An AI agent with MCP can actually do things like, for example, retrieving current sales figures, cross-searching support tickets, initiating workflows – not just talk about them. This is the difference between an AI that's a nifty demo and an AI that's a true teammate that gets work done. Early adopters have shown AI agents performing multi-step tasks like reading code repositories or updating internal knowledge bases. Thanks to MCP, organizations are achieving real productivity gains.

Vendor-neutral and future-proof: MCP is being embraced by major AI players – Anthropic, OpenAI, Microsoft (Copilot Studio) and others – which means it's on track to become a common language for AI integrations. Enterprises will not be locked into a single AI vendor's ecosystem, as a connector designed for the Multi-Cloud Platform (MCP) can work with any compliant AI model. This flexibility allows organizations to switch models without disrupting their existing tool integrations. As the MCP ecosystem continues to mature, we are witnessing the emergence of marketplaces for MCP servers tailored to popular applications like GitHub, Notion and Databricks, which organizations can integrate with minimal effort.

Reduced maintenance and more resilience: Standardizing how AI connects to systems means less brittle code and fewer surprises when things change. MCP essentially decouples the AI from the underlying API changes – if a service updates its API, you only need to update its MCP server, not every AI integration that uses it. It’s also possible to work on versioning and contract evolution so that tools can update without breaking the AI's expectations. This leads to more sustainable, scalable architectures.

There's an additional crucial reason why the MCP plays such an important role for enterprises, and that's security and access control – it's worth focusing on in a separate section.
<h2 id="Security_and_access_control_in_MCP_integrations">Security and access control in MCP integrations</h2>
Any system that connects AI models with sensitive company data must take security seriously. As <a href="https://www.redhat.com/en/blog/model-context-protocol-discover-missing-link-ai-integration" target="_blank" rel="noopener nofollow">Red Hat</a> puts it, the MCP itself is a specification, not a magic security shield. However, it provides a framework that enterprises can implement securely.

MCP servers and their developers are encouraged to adhere to the principle of least privilege. In practice, this means that an MCP server should request only the minimum set of permissions necessary to perform its functions – nothing more. For example, if an AI agent only needs read access to a database to answer queries, the MCP integration should not ask for delete or admin rights.

The guiding rule is simple: the AI agent, through the MCP, operates under the user’s identity and permissions. For instance, if an employee requests an AI assistant to generate a report, the request can be executed via MCP using that employee’s credentials or a delegated access token. This approach ensures that the AI can only access data that the user is permitted to see. This user-based access control is essential in industries with strict data governance, as it prevents an AI solution from inadvertently accessing restricted data.

Moving forward, the community, in line with Red Hat's publication, plans to implement more fine-grained security layers and permission schemas designed explicitly for interactions with AI tools. We may soon see the introduction of governance features for Managed Cloud Platforms (MCP), so administrators can control which tools an AI agent can utilize centrally and the types of data it can access.

Importantly, since MCP connectors are often open-source or auditable, enterprises can inspect and verify an MCP server's actions when it processes data. This level of transparency fosters trust, as organizations do not need to regard AI as a black box that operates without oversight. Instead, there will be verifiable and controlled interfaces for every action the AI performs, which is essential for compliance and security auditing.
<h2 id="MCP_severs_integration">MCP severs integration – an AI-powered platform shift for enterprises</h2>
Aware of the importance of MCP servers, Software Mind developed a custom solution for enterprises that delivers an integration layer between your legacy systems and modern technologies for enterprises. The platform is composed of four main components designed to create a comprehensive multi-agent system:
<ol>
 	<li><strong>LLM &lt;--&gt; Cloud:</strong> This component leverages cloud-based language models to create an intelligent communication layer, enabling advanced interaction and data processing.</li>
 	<li><strong>On-prem:</strong> It offers the flexibility of on-premise deployment, allowing the system to be installed within a client's existing IT environment to ensure data security is maintained.</li>
 	<li><strong>MCP Servers:</strong> This is the core of the platform, acting as a central hub that manages all communication between different systems and seamlessly integrates with existing infrastructure.</li>
 	<li><strong>Multi-Agent Framework:</strong> This component establishes an organized ecosystem where multiple AI agents can collaborate. It functions as a structured system to support and streamline various processes.</li>
</ol>
Our architecture seamlessly combines all layers – from foundational infrastructure and integration with existing systems, up to advanced multi-agent orchestration. This holistic approach enables the creation of secure and highly scalable, enterprise-ready AI solutions that can leverage databases, APIs, storage, and legacy systems as fully integrated tools within your organization. With centralized governance, robust cost control, and comprehensive monitoring built in, organizations gain full visibility and control over every aspect of their AI environment – ensuring compliance, efficiency, and operational excellence at scale.
<h2 id="MCP_servers_evolution">MCP servers – the next stage of AI evolution</h2>
MCP servers and the Model Context Protocol represent a significant leap in integrating AI into enterprise fabric. In the past, organizations struggled to make AI initiatives more than just flashy demos, because connecting AI to real business processes was slow and costly. Now, by building a dedicated integration layer with MCP, companies can deploy AI that is actually useful, from day one.

After the heightened popularity of AI following the generative AI boom, the next phase will focus on how well AI integrates into our existing systems and workflows. MCP servers serve as the bridge between today’s AI and yesterday’s infrastructure.

If you want to leverage MCP servers, modernize your existing tech stack and truly take advantage of AI, contact one of our experts <a href="https://softwaremind.com/contact/">using this form</a>.

<strong>Discover our case studies on AI and learn how we assisted various companies in leveraging this technology:</strong>

<a href="https://softwaremind.com/case-studies/increasing-work-efficiency-and-improving-business-outcomes-with-ai/">Increasing work efficiency and improving business outcomes with artificial intelligence</a>

<a href="https://softwaremind.com/case-studies/building-a-custom-top-rated-b2c-ai-bot/">Building a custom top-rated B2C AI bot</a>

<a href="https://softwaremind.com/case-studies/developing-a-more-immersive-user-experience-with-a-large-language-model/">Developing a pioneering AI-based roleplaying game platform</a><strong>
</strong>

MCP Servers: The Key Integration Layer for Enterprise AI

<div class="tableOfContent">
<div class="tableOfContentHeader">
<h2 class="tableOfContentTitle">Table of contents:</h2>
</div>
<div class="tableOfContentItem"><a href="#Risks_associated_with">
Risks associated with processing file attachments
</a></div>
<div class="tableOfContentItem"><a href="#Examples_of_cyberattacks">
Examples of cyberattacks using file attachments
</a></div>
<div class="tableOfContentItem"><a href="#Dedicated_security_mechanisms">
Dedicated security mechanisms in AWS, Microsoft Azure and GCP
</a></div>
<div class="tableOfContentItem"><a href="#Best_practices_for_safe">
Best practices for safe file attachment management
</a></div>
<div class="tableOfContentItem"><a href="#A_complex_digital">
A complex digital landscape requires a comprehensive approach to cybersecurity
</a></div>
</div>
File attachments are a routine element of business operations, but they can pose serious security risks to any business, ranging from extraction of sensitive data to an organization-wide account takeover. When you allow users to upload files to any of your servers – whether it’s an external or internal system – you have to carefully consider the security measures you have in place. File attachments can be used to escalate attacks, infect devices with malware and ransomware, gain remote access and retrieve data from buckets.

According to <a href="https://blog.talosintelligence.com/content/files/2025/03/2024YiR-report.pdf" target="_blank" rel="noopener">Cisco Talos’ 2024 Year in Review</a> report, 25% of their phishing incident response cases involved malicious attachments. The rising popularity of cloud-based web applications – including contact forms, chats and file repositories – that process file attachments has only increased the threat of attachment-based cyberattacks and the risk of their escalation.

That's why it's essential that companies ensure that file attachments are safely handled, stored and accessed in their cloud-based environments. Read this article to l<strong>earn more about the risks posed by file attachments and explore best practices that can help you reduce these threats.</strong>
<h2 id="Risks_associated_with">Risks associated with processing file attachments</h2>
It’s best to look at the dangers posed by file attachments from different perspectives. On the one hand, malicious actors can exploit user-facing functionalities that enable the upload of files to your server by transferring infected files. But you also need to ensure that your internal shared resources are correctly configured and secured so that, for example, attachments aren’t accessible to anyone on the internet. Here are a few common ways malicious actors can use attachments to compromise your systems.
<h3>Malware and ransomware</h3>
File attachments might contain malicious software, or malware (any software designed to exploit devices, networks and data), and ransomware (software that encrypts data on an infected device for ransom). Ransomware, in particular, has become the most common type of cyberattack nowadays as data encryption followed by a ransom demand can serve as a fast financial gain for organized cybercrime groups. According to <a href="https://arcticwolf.com/resource/aw/the-state-of-cybersecurity-2024-trends-report" target="_blank" rel="noopener">The State of Cybersecurity: 2024 Trends</a> report, <strong>45% of surveyed businesses confirmed they were attacked by ransomware within the previous 12 months.</strong>

As an example, an attacker can send a doctored PDF file that will launch the installation of malware after a user interacts with the file. They can also plant a PDF file prepared specifically to<strong> take advantage of a vulnerability in PDF reader software</strong> (e.g., in 2024, the <a href="https://nvd.nist.gov/vuln/detail/cve-2024-41869" target="_blank" rel="noopener">CVE-2024-41869 vulnerability</a> was often exploited in attacks on Adobe Reader) and execute malicious code on a targeted device.

Other attacks can involve <strong>the lateral movement technique</strong> in which they compromise one entry point (e.g., a service, instance or account) to gain further access within your network. For example, if your S3 bucket is incorrectly configured (e.g., it allows public access or contains authentication data), an attacker could break into your S3 resources to access the connected Lambda and, from there, move to the Relational Database Service (RDS).

In another example, malicious actors could <strong>access your Amazon Web Services (AWS) EC2 instance</strong> and use identity access management (IAM) tokens and roles assigned to this instance to perform actions in other AWS services as well as enter databases and access a subnet (a set of IP addresses in your Amazon Virtual Private Cloud).

Ransomware attacks can result not only in significant security breaches like data theft, but also financial damage. According to <a href="https://arcticwolf.com/resource/aw/arctic-wolf-threat-report-2025" target="_blank" rel="noopener">The Arctic Wolf 2025 Threat Report</a>, the median initial ransom demand across all surveyed industries amounts to $600,000 USD.
<h3>Server-Side Request Forgery (SSRF)</h3>
In SSRF, attackers <strong>force applications to respond to Hypertext Transfer Protocol (HTTP) requests made to internal services to gain unauthorized access to data</strong>, such as EC2 metadata or Google Cloud Platform (GCP) and Azure endpoints. SSRF can be used to view and download files as well as exfiltrate data from internal resources that shouldn’t be otherwise accessed. Attackers can also use the XML external entity injection technique to achieve a similar effect. In this case, a malicious XML file attachment can send a request to an internal resource, which, if processed due to insufficient security measures, will grant attackers access to internal data.
<h3>Data exfiltration</h3>
<strong>Data exfiltration is a method employed by threat actors to illegitimately extract sensitive information from a compromised environment and extract it to an external destination.</strong> The MITRE ATT&amp;CK framework categorizes this tactic into <a href="https://attack.mitre.org/tactics/TA0010/" target="_blank" rel="noopener">nine distinct techniques</a>, detailing the various methods adversaries use to exfiltrate data to outside locations.
<h3>Impersonating a trusted source</h3>
In this technique, file attachments are made to look like they come from a reputable source or someone a user knows. A very common example is <strong>business email compromise (BEC) where attackers target a company’s employees by posing as someone the employee is familiar with</strong> and expects communication from (e.g., getting an invoice from a vendor). The State of Cybersecurity: 2024 Trends report found that 70% of surveyed companies were targets of BEC attack attempts within the previous year.

Often, attackers will first try to access the account of the person or company they want to impersonate, either by infecting their device or acquiring access through spam mechanisms. These phishing emails are usually personalized and carefully crafted to look as legitimate as possible.
<h2 id="Examples_of_cyberattacks">Examples of cyberattacks using file attachments</h2>
Here are a few scenarios that illustrate how file attachments can be exploited by malicious actors to gain unauthorized access to your data and resources.

<strong>1. SSRF that exploits an incorrectly configurated <a href="https://softwaremind.com/blog/what-is-a-web-application-firewall-waf-benefits-functions-and-best-practices/" target="_blank" rel="noopener">web application firewall (WAF)</a></strong>

As explained earlier, attackers can upload an infected file that enables them to access AWS EC2 metadata. As a result, they can download sensitive data from an S3 bucket. This attack is bound to succeed <strong>when compromised resources don't have SSRF defense mechanisms and lack appropriate restrictions for processing HTTP requests.</strong>

A real-world example of this attack can be seen in <a href="https://www.safetydetectives.com/news/pegasus-leak-report/" target="_blank" rel="noopener">the 2022 flight data breach suffered by Pegasus Airlines</a> due to an unprotected AWS S3 bucket. <strong>Lack of password protection for this bucket resulted in 6.5 data, including crew and navigation information, becoming publicly accessible</strong>. Another instance of a SSRF vulnerability was disclosed in 2024 in the <a href="https://nvd.nist.gov/vuln/detail/CVE-2024-21893" target="_blank" rel="noopener">Security Assertion Markup Language (SAML)</a> component of Ivanti Connect Secure devices, which enabled attackers “to access certain restricted resources without authentication.”

<strong>2. Malware in repositories </strong>

Attackers can <strong>plant malware in repositories or ready-made libraries to exploit your resources at a later time</strong>. Malware found in code repositories – especially on platforms like GitHub – often becomes significant threat vectors in attacks on the software supply chain. Threat actors take advantage of these platforms’ trustworthiness and openness to distribute malicious code and attack both individual developers and organizations.

Some examples of malware campaign types include:
<ul>
 	<li><strong>Repo Confusion Campaign</strong> – over 100,000 malicious repositories were transferred to GitHub through obfuscation and social engineering in February 2024.</li>
 	<li><strong>Banana Squad</strong> – the group infected more than 67 Python repositories by imitating hacking tools in 2023.</li>
 	<li><strong>GitVenom</strong> – the campaign conducted in February 2025 resulted in 200+ false repositories containing malware.</li>
</ul>
This problem can also affect an organization’s internal repositories.

<strong>3. Ransomware in invoices</strong>

Departments like accounting that handle many data-heavy documents critical for a company’s operations are often targeted in ransomware attacks. <strong>Attackers can create fake documents to exploit routine attachments</strong>, such as invoices. An employee might unknowingly open an infected file or upload it to an internal network share. A triggered ransomware will encrypt a user’s device, but it could also escalate and spread through cloud-based resources to the entire organization, causing significant financial and reputational damage.

<strong>4. Business email compromise</strong>

Invoices are often similarly used in BEC attacks. These attachments can be sent from accounts employees normally communicate with (for example, vendors or other employees) and include malware or false data. This could<strong> lead to a company’s network being compromised or an employee transferring money to an attacker’s account</strong> that was entered into the falsified invoice. For example, in 2024, <a href="https://www.sec.gov/Archives/edgar/data/1609804/000095014224002170/eh240519238_8k.html" target="_blank" rel="noopener">a manufacturing company lost $60 million in a BEC attack</a> that misled an employee into transferring money to third-party accounts.
<h2 id="Dedicated_security_mechanisms">Dedicated security mechanisms in AWS, Microsoft Azure and GCP</h2>
The top cloud providers – AWS, Microsoft Azure and Google Cloud Platform (GCP) – offer <a href="https://softwaremind.com/services/identity-and-access-management-services/">dedicated security solutions</a> that can be implemented in your cloud environment to mitigate risks and eliminate threats. In this context, it’s important to remember that <strong>it’s the cloud tenant – not the cloud provider – who’s responsible for implementing and configuring the right security mechanisms for their cloud resources.</strong>

Below you’ll find some recommended options that will improve the safety of your cloud-based systems, especially in enterprise or complex environments. Along with some provider-specific features, most of these solutions generally have the same goal: to secure your solutions and help you stay compliant. Keep in mind that it’s a good idea not to rely on only one mechanism. Instead, they should overlap to strengthen your software security at all levels.
<h3>AWS</h3>
<strong>Amazon GuardDuty Malware Protection</strong> automatically scans Elastic Block Store (EBS) and S3 to detect malware in file attachments. It integrates with Amazon Security Hub, which further strengthens verification processes. AWS also offers <strong>a dedicated WAF and the Lambda@Edge functionality</strong>, which can help you better control file attachments and detect dangers at the edge.
<h3>Microsoft Azure</h3>
For an Azure environment, you might want to implement <strong>Microsoft Defender for Storage</strong>, which examines files transferred to Azure Blob Storage for malicious code. It also integrates with Defender for Cloud. Another recommended combination of mechanisms for Azure is <strong>Azure Front Door and Azure Firewall</strong>, which enable you to filter commands and ensure the safety of different application layers. Microsoft also offers <strong>Safe Attachments in Microsoft Defender for Office 365</strong> – an additional security layer for email attachments that have been previously scanned by anti-malware mechanisms in Exchange Online Protection (EOP). This technology uses a virtual environment to check attachments for threats, including malware, ransomware and phishing, before the email is delivered to the addressee.
<h3>GCP</h3>
<strong>Google Security Operations</strong> can be integrated with VirusTotal to analyze files, domains and IP addresses. This integration makes it possible to analyze files in the cloud rather than on user devices, and it gives you access to VirusTotal’s large library of file signatures. Threat detection is based on external results that can be applied to detection rules and alerts. Additionally, C<strong>loud Storage Object Lifecycle Management</strong> can perform specific actions on object classes (e.g., automatically deleting older items), while the Identity and Access Management functions help you control access. Meanwhile, <strong>Cloud Functions and Cloud Armor</strong> enhance the security of custom file processing.
<h2 id="Best_practices_for_safe">Best practices for safe file attachment management</h2>
When processing file attachments, it’s recommended to <strong>implement mechanisms that verify these files</strong>, including their extension and size. Multipurpose Internet Mail Extensions (MIME) validation can help analyze if a file’s content is aligned with its format. When implementing forms where users can upload files, remember to exclude unwanted file extensions and enable the form to verify file formats and sizes and reject files that don’t meet your security criteria.

To protect your company against BEC attacks, you can <strong>apply cloud-native solutions</strong> (e.g., Microsoft Defender for Office 365) and<strong> implement Data Loss Prevention policies</strong>. It’s also important to deploy such email authentication mechanisms as Sender Policy Framework, DomainKeys Identified Mail and Domain-based Message Authentication Reporting and Conformance. These methods protect a sender’s domain from phishing and impersonation attempts, verify sources, ensure integrity and provide policies for responding to unauthorized messages.

<strong>File attachments should also be scanned by antivirus software</strong> for any anomalies or suspicious content before they’re processed and transferred to their target repository. While security mechanisms like the dedicated solutions described earlier might seem like a significant investment, there are some cost-effective alternatives you could adopt, depending on our needs. For example, you can combine AWS Lambda with the open-source antivirus toolkit ClamAV for basic protection.

Moreover, it’s a good idea to <strong>store file attachments in an isolated environment</strong>. For example, you could set up a separate bucket with tighter restrictions to limit potential spread of malware and control access to files.

<strong>Make sure to log and monitor activity in your systems</strong>. Top cloud providers already offer dedicated solutions for security monitoring – AWS has CloudTrail, GCP Audit Logs, and Azure Security Center. This essential practice enables you to more effectively detect anomalies and understand incidents, as part of <a href="https://softwaremind.com/blog/siem-proactively-combating-cyber-threats-and-monitoring-infrastructure-security/">security information and event management (SIEM)</a>.

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<h2 id="A_complex_digital">A complex digital landscape requires a comprehensive approach to cybersecurity</h2>
The risks associated with processing file attachments can turn into serious consequences for companies, ranging from data leaks from a single device to compromised organization-wide infrastructure. While cloud environments provide you with a variety of safety mechanisms to prevent file-based attacks, these solutions require correct configuration.

It’s essential that businesses approach cybersecurity in a holistic way, including adopting a defense-in-depth strategy and implementing layered security to ensure comprehensive system safety at all levels.

If you want to find out how Software Mind’s cybersecurity experts can help secure your applications, <a href="https://softwaremind.com/contact/">reach out via this contact form</a>.

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