Data breaches are alarming incidents where unauthorized access to sensitive data jeopardizes the confidentiality, integrity, and availability of information. They’ve become increasingly common, affecting organizations across the globe. A data breach should be avoided at all costs, and in the event that one occurs it requires swift and thorough mitigation to avoid serious negative consequences. This article explores data breach causes, common types, and repercussions for businesses. We will also outline proactive measures for prevention and effective strategies for mitigation, equipping you with the knowledge you need to protect your organization against this type of cybercrime.
What Is a Data Breach?
A data breach occurs when unauthorized access is gained to confidential information, either through accidental disclosure or deliberate theft. The exposed data often comprises sensitive details like financial records, personal health information, intellectual property, or login credentials, posing significant risks to businesses and their customers.
Businesses must take data security seriously or face legal and financial consequences. Regulations such as the GDPR in Europe and various state-level standards in the US set strict rules for protecting data. Data breaches caused by non-compliance with these regulations can result in substantial fines, as seen in the case of Amazon facing an $886 million penalty for failing to seek consent before using customers’ personal information. Other associated fees include PR and attorney fees, and fallouts from operational disruption.
But data breaches cost companies more than money; they undermine consumer confidence in a business’s ability to protect their sensitive information. As a result, many consumers stop engaging with a brand after a breach, ruining its reputation.
Why Data Breaches Happen
There are several main reasons that data breaches happen, with attackers using a diverse range of methods to seize personal information.
- Loss or theft: If lost, an unencrypted phone, laptop, or drive can be a treasure trove for cybercriminals. Even locked devices may fall prey to sophisticated hacking techniques, opening the door for stored data to be breached.
- Human error and social engineering: Employees might inadvertently expose data by misplacing it, sending it to the wrong recipient, or not securing it properly. Such incidents, though accidental, result in unauthorized data access.
- Insider attacks: Individuals within an organization may intentionally leak data to inflict harm, or benefit financially or personally, by passing information to competitors or hackers. In one case, an employee breached internal systems to give fellow employees paid vacations.
Outsider attacks: Cybercriminals outside an organization target vulnerabilities in its security defenses to gain unauthorized access to sensitive information. These attackers can bypass security measures to steal data for fraudulent purposes or sell it on the dark web through a variety of methods, such as the following:
- Hacking: Hackers find and use weak spots in software or networks to get in and take data without permission. They often look for systems that haven’t been updated with the latest security patches, using their technical skills to bypass security. In 2022, Slack’s GitHub account was reportedly hacked; an attacker stole employee tokens and used them to access company data, turning the hack into a data breach.
Phishing: Here, cybercriminals send fake messages that look like they’re from legitimate sources, such as a well-known parcel delivery service. They then ask recipients to fill in their details and click on a link to track a non-existent package. When the link is clicked, it installs spyware that captures passwords and personal data. This is exactly what happened in 2023 when a Mailchimp employee was tricked into sharing their credentials.
- Malware: Often combined with social engineering or phishing, this method involves tricking someone into installing malware disguised as desirable software. Once installed, this software can take control of the victim’s device, steal sensitive information, and potentially compromise other connected systems, as in a most recent password-stealing malware that spread fake job listings on Facebook in February 2024 in an attempt to gain account credentials. The hacker can also demand payment to unlock them, a tactic known as ransomware, instead of immediately breaching the data.
- DDoS (distributed denial-of-service attack): Unlike other methods that aim to steal data, DDoS attacks can be launched by a group of cybercriminals. The DDoS attack makes data theft difficult or impossible to detect. Rumor has it that the 2020 New Zealand Stock Exchange DDoS attacks were merely a cover for more nefarious cybercrime involving data breaches. Additionally, DDoS attacks can contribute to data breaches indirectly in multiple ways:
- Reduced security focus: During a DDoS attack, security teams are overwhelmed by the surge in traffic, potentially neglecting other security measures that could prevent a data breach.Exploiting vulnerabilities: A DDoS attack might be used to probe a system’s defenses and identify weaknesses that could then be exploited for a data breach.
- Compromised devices: In some cases, the very devices used in a DDoS attack (like hijacked IoT devices) might also be vulnerable to further exploitation, creating a potential entry point for attackers to steal data.
The most common thread tying all these reasons and methods together is security weaknesses.
Which Types of Data Are Commonly Breached?
Cybercriminals use these data breach methods to access and steal different kinds of sensitive information. They target high-value information for financial gain through selling this data on the black market, or cause direct harm to individuals or organizations by using the stolen information for identity theft or fraud.
Financial Information
Attackers commonly seek to steal bank account details and credit card numbers. One example is the Equifax breach in 2017, where attackers exploited outdated third-party software to expose the financial data of over 153 million individuals. This breach not only led to financial losses for those affected but also damaged their credit standings.
Personal Information
Identity theft breaches focus on personal information, such as names and social security numbers, allowing criminals to assume another person’s identity. The massive Yahoo breach affected up to 1.5 billion accounts in 2013, with hackers stealing email addresses and security questions, opening the door to fraudulent activities under the victims’ names.
Health Information
Unauthorized access to medical histories and insurance information reached new heights in 2023 with 133 million records exposed through 26 breaches. Among these, the largest breach impacted over 11 million individuals, marking it as the second-largest healthcare data breach ever recorded.
Intellectual Property
Another type of data targeted in breaches is intellectual property, which includes patents, trade secrets, blueprints, customer lists, and contracts. The theft of such sensitive information undermines innovation and can give competitors an unfair advantage, disrupting market dynamics and potentially leading to a drop in stock prices.
How to Prevent Data Breaches
Preventing data breaches is proactive; prevention focuses on stopping breaches before they happen, unlike reactive measures that address or mitigate the issue after a breach occurs (more on that later.) Prevention starts with a strong awareness of baseline security measures that are required for protecting sensitive information.
Let’s review some best practices, categorized by data breach type.
Protect Against Potential Loss or Theft
Proactively safeguard your data in case a loss or theft occurs, by encrypting your data. Encryption scrambles information, making it unreadable without a special key. Encrypt data on laptops, desktops, and mobile devices using built-in security features or third-party encryption software to turn your sensitive information into a code that only someone with the correct key can unlock. This way, even if stolen, your data remains useless to thieves.
It’s also recommended to use strong, unique passwords and multi-factor authentication (MFA) for all user accounts. MFA requires a second verification code, like a text message or fingerprint scan, to access accounts. This makes stolen passwords less valuable to attackers.
Other potentially powerful additions to your security are user behavior analytics (UBA) and traffic analysis capabilities. Empowered by AI, your security system will then be capable of pinpointing malicious traffic and distinguishing legitimate traffic, such as search engine, copyright, and site monitoring bots, from malicious botnet activity.
Preempt Human Error
Within organizations, regular security awareness training equips employees to identify phishing attempts (fake emails or websites designed to steal information). Training also teaches them to avoid suspicious links and attachments and handle sensitive information with care. Individuals can also find online courses to help them spot suspicious activity before they click on a dangerous link, download crippling malware, or unintentionally share sensitive information with unauthorized people.
Establishing and enforcing clear policies for secure data sharing and storage can also significantly reduce the chances of accidental data exposure. Organizations should only grant access to data based on the “need-to-know” principle, meaning only those whose roles specifically require it should have access.
Prevent Malicious Insider or Outsider Attacks
Now that you’ve protected against loss and theft, and have implemented training to preempt human error, here are additional steps you should take to prevent malicious attacks, led by your IT Security team:
- Patch your software regularly: Software updates often include security patches that fix vulnerabilities that hackers were previously able to exploit. Updating all software and operating systems promptly closes these security gaps and strengthens your defenses. Perform penetration testing to determine the need for patches.
- Deploy firewalls, IDS, and encryption: Firewalls like Gcore WAAP act as a barrier between your network and the internet, blocking unauthorized access attempts. Intrusion Detection Systems (IDS) monitor your network for suspicious activity, like attempted breaches. Encryption adds an extra layer of protection, making stolen data unreadable.
- Regularly back up your data to a secure location: Routinely save copies of your data to an external hard drive, cloud service, or another secure off-site storage to ensure that a duplicate exists. This allows you to restore critical information in case of a breach or other disaster.
- Operate according to a zero-trust model: Enable and teach employees to adopt a zero-trust model, which assumes no user or device is inherently trustworthy and requires verification for all access attempts. This extra layer of caution helps prevent unauthorized access, even if an employee is tricked.
- Develop and test a disaster recovery plan: Outline steps to mitigate a security incident and resume operations quickly.
- Train your staff: Regularly train employees on best practices for data security and the importance of protecting sensitive information. This includes awareness about phishing, proper data handling, and secure device use.
How to Mitigate Data Breaches
While prevention is ideal, even the most secure systems can be subject to human error and breach. Here’s a quick response guide to minimize damage if a breach occurs:
- Detect: Implement a robust software detection system to monitor for signs of illicit access or suspicious activities, such as unusual login attempts or surprise data movements. Gcore WAAP actively scans incoming traffic in real time, ensuring swift detection of potential breach attempts. Security Information and Event Management (SIEM) systems can also become a powerful addition to your security posture.
- Contain: Once a breach is detected, the top priority is to contain it and prevent further damage. WAAP automatically blocks and isolates data identified as part of a zero-day or OWASP list of attacks, as well as a custom policies list. It is capable of detecting malicious traffic and evasive bots, minimizing the risk of unauthorized access.
- Eradicate: With the breach contained, IT team members must eliminate the underlying security weakness by removing malware, closing unauthorized access points, and patching vulnerabilities.
- Report and recover: Have your IT team communicate the breach to affected parties and develop a recovery plan that includes restoring lost data and updating your policies to reflect lessons learned. Work with Gcore’s security experts to develop a protection plan to avoid future breaches, you can book a free demo and get started today.
Conclusion
Data breaches can be devastating, but they’re preventable. Distinguishing between accidental and malicious data breaches and the methods used to execute them can help you develop a layered defense strategy that enhances your organization’s security posture and keeps personal, financial, and other customer data in your rightful hands.
Looking for a comprehensive data security solution to help prevent data breaches? Gcore WAAP protects against all types of hacks, from zero-day to those on the OWASP list, that can lead to hacking and data compromise. As a result, WAAP helps ensure your business is safe from data breaches, protecting your organizational reputation and your bottom line.
Related articles
What is cloud security? Definition, challenges, and best practices
Cloud security is the discipline of protecting cloud-based infrastructure, applications, and data from internal and external threats, ensuring confidentiality, integrity, and availability of cloud resources. This protection model has become important as organizations increasingly move their operations to cloud environments.Cloud security operates under a shared responsibility model where providers secure the infrastructure while customers secure their deployed applications, data, and access policies. This responsibility distribution varies by service model, with Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) each requiring different levels of customer involvement.The model creates clear boundaries between provider and customer security obligations.Cloud security protects resources and data individually rather than relying on a traditional perimeter defense approach. This protection method uses granular controls like cloud security posture management (CSPM), network segmentation, and encryption to secure specific assets. The approach addresses the distributed nature of cloud computing, where resources exist across multiple locations and services.Organizations face several cloud security challenges, including misconfigurations, account hijacking, data breaches, and insider threats.Cloud security matters because the average cost of a cloud data breach has reached $5 million according to IBM, making effective security controls essential for protecting both financial assets and organizational reputation.What is cloud security?Cloud security is the practice of protecting cloud-based infrastructure, applications, and data from cyber threats through specialized technologies, policies, and controls designed for cloud environments. This protection operates under a shared responsibility model where cloud providers secure the underlying infrastructure while customers protect their applications, data, and access configurations.Cloud security includes identity and access management (IAM), data encryption, continuous monitoring, workload protection, and automated threat detection to address the unique challenges of distributed cloud resources. The approach differs from traditional security by focusing on individual resource protection rather than perimeter defense, as cloud environments require granular controls and real-time visibility across flexible infrastructure.How does cloud security work?Cloud security works by using a multi-layered defense system that protects data, applications, and infrastructure hosted in cloud environments through shared responsibility models, identity controls, and continuous monitoring. Unlike traditional perimeter-based security, cloud security operates on a distributed model where protection is applied at multiple levels across the cloud stack.The foundation of cloud security rests on the shared responsibility model, where cloud providers secure the underlying infrastructure while customers protect their applications, data, and access policies. This division varies by service type - in Infrastructure as a Service (IaaS), customers handle more security responsibilities, including operating systems and network controls. In contrast, Software as a Service (SaaS) shifts most security duties to the provider.Identity and Access Management (IAM) serves as the primary gatekeeper, controlling who can access cloud resources and what actions they can perform.IAM systems use role-based access control (RBAC) and multi-factor authentication (MFA) to verify user identities and enforce least-privilege principles. These controls prevent unauthorized access even if credentials are compromised.Data protection operates through encryption both at rest and in transit, ensuring information remains unreadable to unauthorized parties. Cloud security platforms also employ workload protection agents that monitor running applications for suspicious behavior. At the same time, Security Information and Event Management (SIEM) systems collect and analyze logs from across the cloud environment to detect potential threats.Continuous monitoring addresses the flexible nature of cloud environments, where resources are constantly created, modified, and destroyed.Cloud Security Posture Management (CSPM) tools automatically scan configurations against security best practices, identifying misconfigurations that could expose data.What are the main cloud security challenges?Cloud security challenges refer to the obstacles and risks that organizations face when protecting their cloud-based infrastructure, applications, and data from threats. The main cloud security challenges are listed below.Misconfigurations: According to Zscaler research, improper cloud settings create the most common security vulnerabilities, with 98.6% of organizations having misconfigurations that cause critical risks to data and infrastructure. These include exposed storage buckets, overly permissive access controls, and incorrect network settings.Shared responsibility confusion: Organizations struggle to understand which security tasks belong to the cloud provider versus what their own responsibilities are. This confusion leads to security gaps where critical protections are assumed to be handled by the other party.Identity and access management complexity: Managing user permissions across multiple cloud services and environments becomes difficult as organizations scale. Weak authentication, excessive privileges, and poor access controls create entry points for attackers.Data protection across environments: Securing sensitive data as it moves between on-premises systems, multiple cloud platforms, and edge locations requires consistent encryption and monitoring. Organizations often lack visibility into where their data resides and how it's protected.Compliance and regulatory requirements: Meeting industry standards like GDPR, HIPAA, or SOC 2 becomes more complex in cloud environments where data location and processing methods may change flexibly. Organizations must maintain compliance across multiple jurisdictions and service models.Limited visibility and monitoring: Traditional security tools often can't provide complete visibility into cloud workloads, containers, and serverless functions. This blind spot makes it difficult to detect threats, track user activities, and respond to incidents quickly.Insider threats and privileged access: Cloud environments often grant broad administrative privileges that can be misused by malicious insiders or compromised accounts. The distributed nature of cloud access makes it harder to monitor and control privileged user activities.What are the essential cloud security technologies and tools?Essential cloud security technologies and tools refer to the specialized software, platforms, and systems designed to protect cloud-based infrastructure, applications, and data from cyber threats and operational risks. The essential cloud security technologies and tools are listed below.Identity and access management (IAM): IAM systems control who can access cloud resources and what actions they can perform through role-based permissions and multi-factor authentication. These platforms prevent unauthorized access by requiring users to verify their identity through multiple methods before granting system entry.Cloud security posture management (CSPM): CSPM tools continuously scan cloud environments to identify misconfigurations, compliance violations, and security gaps across multiple cloud platforms. They provide automated remediation suggestions and real-time alerts when security policies are violated or resources are improperly configured.Data encryption services: Encryption technologies protect sensitive information both at rest in storage systems and in transit between cloud services using advanced cryptographic algorithms. These tools mean that even if data is intercepted or accessed without authorization, it remains unreadable without proper decryption keys.Cloud workload protection platforms (CWPP): CWPP solutions monitor and secure applications, containers, and virtual machines running in cloud environments against malware, vulnerabilities, and suspicious activities. They provide real-time threat detection and automated response capabilities specifically designed for flexible cloud workloads.Security information and event management (SIEM): Cloud-based SIEM platforms collect, analyze, and correlate security events from across cloud infrastructure to detect potential threats and compliance violations. These systems use machine learning and behavioral analysis to identify unusual patterns that may indicate security incidents.Cloud access security brokers (CASB): CASB solutions act as intermediaries between users and cloud applications, enforcing security policies and providing visibility into cloud usage across the organization. They monitor data movement, detect risky behaviors, and ensure compliance with regulatory requirements for cloud-based activities.Network security tools: Cloud-native firewalls and network segmentation tools control traffic flow between cloud resources and external networks using intelligent filtering rules. These technologies create secure network boundaries and prevent lateral movement of threats within cloud environments.What are the key benefits of cloud security?The key benefits of cloud security refer to the advantages organizations gain from protecting their cloud-based infrastructure, applications, and data from threats. The key benefits of cloud security are listed below.Cost reduction: Cloud security eliminates the need for expensive on-premises security hardware and reduces staffing requirements. Organizations can access enterprise-grade security tools through subscription models rather than large capital investments.Improved threat detection: Cloud security platforms use machine learning and AI to identify suspicious activities in real-time across distributed environments. These systems can detect anomalies that traditional security tools might miss.Automatic compliance: Cloud security solutions help organizations meet regulatory requirements like GDPR, HIPAA, and SOC 2 through built-in compliance frameworks. Automated reporting and audit trails simplify compliance management and reduce manual oversight.Reduced misconfiguration risks: Cloud security posture management tools automatically scan for misconfigurations and provide remediation guidance.Enhanced data protection: Cloud security provides multiple layers of encryption for data at rest, in transit, and in use. Advanced key management systems ensure that sensitive information remains protected even if other security measures fail.Flexible security coverage: Cloud security solutions automatically scale with business growth without requiring additional infrastructure investments. Organizations can protect new workloads and applications instantly as they use them.Centralized security management: Cloud security platforms provide unified visibility across multiple cloud environments and hybrid infrastructures. Security teams can monitor, manage, and respond to threats from a single dashboard rather than juggling multiple tools.What are the challenges of cloud security?Cloud security challenges refer to the obstacles and risks organizations face when protecting their cloud-based infrastructure, applications, and data from threats. These challenges are listed below.Misconfigurations: Cloud environments are complex, and improper settings create security gaps that attackers can exploit. These errors include exposed storage buckets, overly permissive access controls, and incorrect network settings.Shared responsibility confusion: Organizations often misunderstand which security tasks belong to them versus their cloud provider. This confusion leads to gaps where critical security measures aren't implemented by either party. The division of responsibilities varies between IaaS, PaaS, and SaaS models, adding to the complexity.Identity and access management complexity: As organizations scale, managing user permissions across multiple cloud services and environments becomes difficult. Weak authentication methods and excessive privileges create entry points for unauthorized access. Multi-factor authentication and role-based access controls require careful planning and ongoing maintenance.Data protection across environments: Ensuring data remains encrypted and secure as it moves between on-premises systems and cloud platforms presents ongoing challenges. Organizations must track data location, apply appropriate encryption, and maintain compliance across different jurisdictions. Data residency requirements add another layer of complexity.Visibility and monitoring gaps: Traditional security tools often can't provide complete visibility into cloud environments and workloads. The flexible nature of cloud resources makes it hard to track all assets and their security status. Real-time monitoring becomes critical but technically challenging to use effectively.Compliance and regulatory requirements: Meeting industry standards and regulations in cloud environments requires continuous effort and specialized knowledge. Different regions have varying data protection laws that affect cloud deployments. Organizations must prove compliance while maintaining operational effectiveness.Insider threats and privileged access: Cloud environments often grant broad access to administrators and developers, creating risks from malicious or careless insiders. Monitoring privileged user activities without impacting productivity requires advanced tools and processes. The remote nature of cloud access makes traditional oversight methods less effective.How to implement cloud security best practices?You use cloud security best practices by establishing a complete security framework that covers identity management, data protection, monitoring, and compliance across your cloud environment.First, configure identity and access management (IAM) with role-based access control (RBAC) and multi-factor authentication (MFA). Create specific roles for different job functions and require MFA for all administrative accounts to prevent unauthorized access.Next, encrypt all data both at rest and in transit using industry-standard encryption protocols like AES256.Enable encryption for databases, storage buckets, and communication channels between services to protect sensitive information from interception.Then, use continuous security monitoring with automated threat detection tools. Set up real-time alerts for suspicious activities, failed login attempts, and unusual data access patterns to identify potential security incidents quickly.After that, establish cloud security posture management (CSPM) to scan for misconfigurations automatically. Configure automated remediation for common issues like open security groups, unencrypted storage, and overly permissive access policies.Create network segmentation using virtual private clouds (VPCs) and security groups to isolate different workloads. Limit communication between services to only what's necessary and use zero-trust network principles.Set up regular security audits and compliance monitoring to meet industry standards like SOC 2, HIPAA, or GDPR. Document all security controls and maintain audit trails for regulatory requirements.Finally, develop an incident response plan specifically for cloud environments. Include procedures for isolating compromised resources, preserving forensic evidence, and coordinating with your cloud provider's security team.Start with IAM and encryption as your foundation, then build additional security layers progressively to avoid overwhelming your team while maintaining strong protection.Gcore cloud securityWhen using cloud security measures, the underlying infrastructure becomes just as important as the security tools themselves. Gcore’s cloud security solutions address this need with a global network of 180+ points of presence and 30ms latency, ensuring your security monitoring and threat detection systems perform consistently across all regions. Our edge cloud infrastructure supports real-time security analytics and automated threat response without the performance bottlenecks that can leave your systems vulnerable during critical moments.What sets our approach apart is the combination of security directly into the infrastructure layer, eliminating the complexity of managing separate security vendors while providing enterprise-grade DDoS protection and encrypted data transmission as standard features. This unified approach typically reduces security management overhead by 40-60% compared to multi-vendor solutions, while maintaining the continuous monitoring capabilities.Explore how Gcore's integrated cloud security infrastructure can strengthen your defense plan at gcore.com/cloud.Frequently asked questionsWhat's the difference between cloud security and traditional approaches?Cloud security differs from traditional approaches by protecting distributed resources through shared responsibility models and cloud-native tools, while traditional security relies on perimeter-based defenses around centralized infrastructure. Traditional security assumes a clear network boundary with firewalls and intrusion detection systems protecting internal resources. In contrast, cloud security secures individual workloads, data, and identities across multiple environments without relying on network perimeters.What is cloud security posture management?Cloud security posture management (CSPM) is a set of tools and processes that continuously monitor cloud environments to identify misconfigurations, compliance violations, and security risks across cloud infrastructure. CSPM platforms automatically scan cloud resources, assess security policies, and provide remediation guidance to maintain proper security configurations.How does Zero Trust apply to cloud security?Zero Trust applies to cloud security by treating every user, device, and connection as untrusted and requiring verification before granting access to cloud resources. This approach replaces traditional perimeter-based security with continuous authentication, micro-segmentation, and least-privilege access controls across cloud environments.What compliance standards apply?Cloud security must comply with industry-specific regulations like SOC 2, ISO 27001, GDPR, HIPAA, PCI DSS, and FedRAMP, depending on your business sector and geographic location. Organizations typically need to meet multiple standards simultaneously, with financial services requiring PCI DSS compliance, healthcare needing HIPAA certification, and EU operations mandating GDPR adherence.What happens during a cloud security breach?During a cloud security breach, attackers gain unauthorized access to cloud resources, potentially exposing sensitive data, disrupting services, and causing financial damage averaging $5 million per incident, according to IBM. The breach typically involves exploiting misconfigurations, compromised credentials, or vulnerabilities to access cloud infrastructure, applications, or data stores.
Query your cloud with natural language: A developer’s guide to Gcore MCP
What if you could ask your infrastructure questions and get real answers?With Gcore’s open-source implementation of the Model Context Protocol (MCP), now you can. MCP turns generative AI into an agent that understands your infrastructure, responds to your queries, and takes action when you need it to.In this post, we’ll demo how to use MCP to explore and inspect your Gcore environment just by prompting, to list resources, check audit logs, and generate cost reports. We’ll also walk through a fun bonus use case: provisioning infrastructure and exporting it to Terraform.What is MCP and why do devs love it?Originally developed by Anthropic, the Model Context Protocol (MCP) is an open standard that turns language models into agents that interact with structured tools: APIs, CLIs, or internal systems. Gcore’s implementation makes this protocol real for our customers.With MCP, you can:Ask questions about your infrastructureList, inspect, or filter cloud resourcesView cost data, audit logs, or deployment metadataExport configs to TerraformChain multi-step operations via natural languageGcore MCP removes friction from interacting with your infrastructure. Instead of wiring together scripts or context-switching across dashboards and CLIs, you can just…ask.That means:Faster debugging and auditsMore accessible infra visibilityFewer repetitive setup tasksBetter team collaborationBecause it’s open source, backed by the Gcore Python SDK, you can plug it into other APIs, extend tool definitions, or even create internal agents tailored to your stack. Explore the GitHub repo for yourself.What can you do with it?This isn’t just a cute chatbot. Gcore MCP connects your cloud to real-time insights. Here are some practical prompts you can use right away.Infrastructure inspection“List all VMs running in the Frankfurt region”“Which projects have over 80% GPU utilization?”“Show all volumes not attached to any instance”Audit and cost analysis“Get me the API usage for the last 24 hours”“Which users deployed resources in the last 7 days?”“Give a cost breakdown by region for this month”Security and governance“Show me firewall rules with open ports”“List all active API tokens and their scopes”Experimental automation“Create a secure network in Tokyo, export to Terraform, then delete it”We’ll walk through that last one in the full demo below.Full video demoWatch Gcore’s AI Software Engineer, Algis Dumbris, walk through setting up MCP on your machine and show off some use cases. If you prefer reading, we’ve broken down the process step-by-step below.Step-by-step walkthroughThis section maps to the video and shows exactly how to replicate the workflow locally.1. Install MCP locally (0:00–1:28)We use uv to isolate the environment and pull the project directly from GitHub.curl -Ls https://astral.sh/uv/install.sh | sh uvx add gcore-mcp-server https://github.com/G-Core/gcore-mcp-server Requirements:PythonGcore account + API keyTool config file (from the repo)2. Set up your environment (1:28–2:47)Configure two environment variables:GCORE_API_KEY for authGCORE_TOOLS to define what the agent can access (e.g., regions, instances, costs, etc.)Soon, tool selection will be automatic, but today you can define your toolset in YAML or JSON.3. Run a basic query (3:19–4:11)Prompt:“Find the Gcore region closest to Antalya.”The agent maps this to a regions.list call and returns: IstanbulNo need to dig through docs or write an API request.4. Provision, export, and clean up (4:19–5:32)This one’s powerful if you’re experimenting with CI/CD or infrastructure-as-code.Prompt:“Create a secure network in Tokyo. Export to Terraform. Then clean up.”The agent:Provisions the networkExports it to Terraform formatDestroys the resources afterwardYou get usable .tf output with no manual scripting. Perfect for testing, prototyping, or onboarding.Gcore: always building for developersTry it now:Clone the repoInstall UVX + configure your environmentStart prompting your infrastructureOpen issues, contribute tools, or share your use casesThis is early-stage software, and we’re just getting started. Expect more tools, better UX, and deeper integrations soon.Watch how easy it is to deploy an inference instance with Gcore
How to protect login pages with Gcore WAAP
Exposed login pages are a common vulnerability across web applications. Attackers often use automated tools to guess credentials in brute-force or credential-stuffing attacks, probe for login behavior to exploit session or authentication logic, or overload your infrastructure with fake requests.Without specific rules for login-related traffic, your application might miss these threats or apply overly broad protections that disrupt real users. Fortunately, Gcore WAAP makes it easy to defend these sensitive endpoints without touching your application code.In this guide, we’ll show you how to use WAAP’s custom rule engine to identify login traffic and apply protections like CAPTCHA to reduce risk, block automated abuse, and maintain a smooth experience for legitimate users. We’ve also included a complete video walkthrough from Gcore’s Security Presales Engineer, Michal Zalewski.Video walkthroughHere’s Gcore’s Michal Zalewski giving a full walkthrough of the steps in this article.Step 1: Access your WAAP configurationGo to portal.gcore.com and log in.Navigate to WAAP in the sidebar. If you’re not yet a WAAP user, it costs just $26/month.Select the resource that hosts your login form; for example, gcore.zalewski.cloud.Step 2: Create a custom ruleIn the main panel of your selected resource, go to WAAP Rules.Click Add Custom Rule in the upper-right corner.Step 3: Define the login page URLIdentify the login endpoint you want to protect:Use tools like Burp Suite or the "Inspect" feature in your browser to verify the login page URL.In Burp Suite, use the Proxy tab, or in the browser, check the Network tab to inspect a login request.Look for the path (e.g., /login.php) and HTTP method (POST).In the custom rule setup:Enter the URL (e.g., /login.php).Tag the request using a predefined tag. Select Login Page.Step 4: Name and save the ruleProvide a name for the rule, such as “Login Page URL”, and save it.Step 5: Add a CAPTCHA challenge ruleTo protect the login page from automated abuse:Create a new custom rule.Name it something like “Login Page Challenge”.Under Conditions, select the previously created Login Page tag.Set the Action to CAPTCHA.Save the rule.Step 6: Test the protectionReturn to your browser and turn off any proxy tools.Refresh the login page.You should now be challenged with a CAPTCHA each time the login page loads.Once the CAPTCHA is completed successfully, users can log in as usual.Monitor, adapt, and alertAfter deployment:Track rate limit trigger frequencyMonitor WAAP logs for anomaly detectionRotate exemptions or thresholds based on live behaviorFor analytics, refer to the WAAP analytics documentation.Bonus tips for hardened protectionCombine with bot protection: Enable WAAP’s bot mitigation to identify headless browsers and automation tools like Puppeteer or Selenium. See our bot protection docs for setup instructions.Customize 429 responses: Replace default error pages with branded messages or a fallback action. Consider including a support link or CAPTCHA challenge. Check out our response pages documentation for more details.Use geo or ASN exceptions: Whitelist trusted locations or block known bot-heavy ASNs if your audience is localized.Automate it: optional API and Terraform supportTeams with IaC pipelines or security automation workflows might want to automate login page protection with rate limiting. This keeps your WAAP config version-controlled and repeatable.You can use the WAAP API or Terraform to:Create or update rulesRotate session keys or thresholdsExport logs for auditingExplore the WAAP API documentation and WAAP Terraform provider documentation for more details.Stop abuse before it starts with GcoreLogin pages are high-value targets, but they don’t have to be high risk. With Gcore WAAP, setting up robust defenses takes just a few minutes. By tagging login traffic and applying challenge rules like CAPTCHA, you can reduce automated attack risk without sacrificing user experience.As your application grows, revisit your WAAP rules regularly to adapt to new threats, add behavior-based detection, and fine-tune your protective layers. For more advanced configurations, check out our documentation or reach out to Gcore support.Get WAAP today for just $26/month
3 underestimated security risks of AI workloads and how to overcome them
Artificial intelligence workloads introduce a fundamentally different security landscape for engineering and security teams. Unlike traditional applications, AI systems must protect not just endpoints and networks, but also training data pipelines, feature stores, model repositories, and inference APIs. Each phase of the AI life cycle presents distinct attack vectors that adversaries can exploit to corrupt model behavior, extract proprietary logic, or manipulate downstream outputs.In this article, we uncover three security vulnerabilities of AI workloads and explain how developers and MLOps teams can overcome them. We also look at how investing in your AI security can save time and money, explore the challenges that lie ahead for AI security, and offer a simplified way to protect your AI workloads with Gcore.Risk #1: data poisoningData poisoning is a targeted attack on the integrity of AI systems, where malicious actors subtly inject corrupted or manipulated data into training pipelines. The result is a model that behaves unpredictably, generates biased or false outputs, or embeds hidden logic that can be triggered post-deployment. This can undermine business-critical applications—from fraud detection and medical diagnostics to content moderation and autonomous decision-making.For developers, the stakes are high: poisoned models are hard to detect once deployed, and even small perturbations in training data can have system-wide consequences. Luckily, you can take a few steps to mitigate against data poisoning and then implement zero-trust AI to further protect your workloads.Mitigation and hardeningRestrict dataset access using IAM, RBAC, or identity-aware proxies.Store all datasets in versioned, signed, and hashed formats.Validate datasets with automated schema checks, label distribution scans, and statistical outlier detection before training.Track data provenance with metadata logs and checksums.Block training runs if datasets fail predefined data quality gates.Integrate data validation scripts into CI/CD pipelines pre-training.Enforce zero-trust access policies for data ingestion services.Solution integration: zero-trust AIImplement continuous authentication and authorization for each component interacting with data (e.g., preprocessing scripts, training jobs).Enable real-time threat detection during training using runtime security tools.Automate incident response triggers for unexpected file access or data source changes.Risk #2: adversarial attacksAdversarial attacks manipulate model inputs in subtle ways that trick AI systems into making incorrect or dangerous decisions. These perturbations—often imperceptible to humans—can cause models to misclassify images, misinterpret speech, or misread sensor data. In high-stakes environments like facial recognition, autonomous vehicles, or fraud detection, these failures can result in security breaches, legal liabilities, or physical harm.For developers, the threat is real: even state-of-the-art models can be easily fooled without adversarial hardening. The good news? You can make your models more robust by combining defensive training techniques, input sanitization, and secure API practices. While encrypted inference doesn’t directly block adversarial manipulation, it ensures that sensitive inference data stays protected even if attackers attempt to probe the system.Mitigation and hardeningUse adversarial training frameworks like CleverHans or IBM ART to expose models to perturbed inputs during training.Apply input sanitization layers (e.g., JPEG re-encoding, blurring, or noise filters) before data reaches the model.Implement rate limiting and authentication on inference APIs to block automated adversarial probing.Use model ensembles or randomized smoothing to improve resilience to small input perturbations.Log and analyze input-output patterns to detect high-variance or abnormal responses.Test models regularly against known attack vectors using robustness evaluation tools.Solution integration: encrypted inferenceWhile encryption doesn't prevent adversarial inputs, it does mean that input data and model responses remain confidential and protected from observation or tampering during inference.Run inference in trusted environments like Intel SGX or AWS Nitro Enclaves to protect model and data integrity.Use homomorphic encryption or SMPC to process encrypted data without exposing sensitive input.Ensure that all intermediate and output data is encrypted at rest and in transit.Deploy access policies that restrict inference to verified users and approved applications.Risk #3: model leakage of intellectual assetsModel leakage—or model extraction—happens when an attacker interacts with a deployed model in ways that allow them to reverse-engineer its structure, logic, or parameters. Once leaked, a model can be cloned, monetized, or used to bypass the very defenses it was meant to enforce. For businesses, this means losing competitive IP, compromising user privacy, or enabling downstream attacks.For developers and MLOps teams, the challenge is securing deployed models in a way that balances performance and privacy. If you're exposing inference APIs, you’re exposing potential entry points—but with the right controls and architecture, you can drastically reduce the risk of model theft.Mitigation and hardeningEnforce rate limits and usage quotas on all inference endpoints.Monitor for suspicious or repeated queries that indicate model extraction attempts.Implement model watermarking or fingerprinting to trace unauthorized model use.Obfuscate models before deployment using quantization, pruning, or graph rewriting.Disable or tightly control any model export functionality in your platform.Sign and verify inference requests and responses to ensure authenticity.Integrate security checks into CI/CD pipelines to detect risky configurations—such as public model endpoints, export-enabled containers, or missing inference authentication—before they reach production.Solution integration: native security integrationIntegrate model validation, packaging, and signing into CI/CD pipelines.Serve models from encrypted containers or TEEs, with minimal runtime exposure.Use container and image scanning tools to catch misconfigurations before deployment.Centralize monitoring and protection with tools like Gcore WAAP for real-time anomaly detection and automated response.How investing in AI security can save your business moneyFrom a financial point of view, the use of AI and machine learning in cybersecurity can lead to massive cost savings. Organizations that utilize AI and automation in cybersecurity have saved an average of $2.22 million per data breach compared to organizations that do not have these protections in place. This is because the necessity for manual oversight is reduced, lowering the total cost of ownership, and averting costly security breaches. The initial investment in advanced security technologies yields returns through decreased downtime, fewer false positives, and an enhanced overall security posture.Challenges aheadWhile securing the AI lifecycle is essential, it’s still difficult to balance robust security with a positive user experience. Rigid scrutiny can add additional latency or false positives that can stop operations, but AI-powered security can avoid such incidents.Another concern organizations must contend with is how to maintain current AI models. With threats changing so rapidly, today's newest model could easily become outdated by tomorrow’s. Solutions must have an ongoing learning ability so that security detection parameters can be revised.Operational maturity is also a concern, especially for companies that operate in multiple geographies. Well-thought-out strategies and sound governance processes must accompany the integration of complex AI/ML tools with existing infrastructure, but automation still offers the most benefits by reducing the overhead on security teams and helping ensure consistent deployment of security policies.Get ahead of AI security with GcoreAI workloads introduce new and often overlooked security risks that can compromise data integrity, model behavior, and intellectual property. By implementing practices like zero-trust architecture, encrypted inference, and native security integration, developers can build more resilient and trustworthy AI systems. As threats evolve, staying ahead means embedding security at every phase of the AI lifecycle.Gcore helps teams apply these principles at scale, offering native support for zero-trust AI, encrypted inference, and intelligent API protection. As an experienced AI and security solutions provider, our DDoS Protection and AI-enabled WAAP solutions integrate natively with Everywhere Inference and GPU Cloud across 210+ global points of presence. That means low latency, high performance, and proven, robust security, no matter where your customers are located.Talk with our AI security experts and secure your workloads today
Flexible DDoS mitigation with BGP Flowspec
For customers who understand their own network traffic patterns, rigid DDoS protection can be more of a limitation than a safeguard. That’s why Gcore supports BGP Flowspec: a flexible, standards-based method for defining granular filters that block or rate-limit malicious traffic in real time…before it reaches your infrastructure.In this article, we’ll walk through:What Flowspec is and how it worksThe specific filters and actions Gcore supportsCommon use cases, with example rule definitionsHow to activate and monitor Flowspec in your environmentWhat is the BGP Flowspec?BGP Flowspec (RFC 8955) extends Border Gateway Protocol to distribute traffic filtering rules alongside routing updates. Instead of static ACLs or reactive blackholing, Flowspec enables near-instantaneous propagation of mitigation rules across networks.BGP tells routers how to reach IP prefixes across the internet. With Flowspec, those same BGP announcements can now carry rules, not just routes. Each rule describes a pattern of traffic (e.g., TCP SYN packets >1000 bytes from a specific subnet) and what action to take (drop, rate-limit, mark, or redirect).What are the benefits of the BGP Flowspec?Most traditional DDoS protection services react to threats after they start, whether by blackholing traffic to a target IP, redirecting flows to a scrubbing center, or applying rigid, static filters. These approaches can block legitimate traffic, introduce latency, or be too slow to respond to fast-evolving attacks.Flowspec offers a more flexible alternative.Proactive mitigation: Instead of waiting for attacks, you can define known-bad traffic patterns ahead of time and block them instantly. Flowspec lets experienced operators prevent incidents before they start.Granular filtering: You’re not limited to blocking by IP or port. With Flowspec, you can match on packet size, TCP flags, ICMP codes, and more, enabling fine-tuned control that traditional ACLs or RTBH don’t support.Edge offloading: Filtering happens directly on Gcore’s routers, offloading your infrastructure and avoiding scrubbing latency.Real-time updates: Changes to rules are distributed across the network via BGP and take effect immediately, faster than manual intervention or standard blackholing.You still have the option to block traffic during an active attack, but with Flowspec, you gain the flexibility to protect services with minimal disruption and greater precision than conventional tools allow.Which parts of the Flowspec does Gcore implement?Gcore supports twelve filter types and four actions of the Flowspec.Supported filter typesGcore supports all 12 standard Flowspec match components.Filter FieldDescriptionDestination prefixTarget subnet (usually your service or app)Source prefixSource of traffic (e.g., attacker IP range)IP protocolTCP, UDP, ICMP, etc.Port / Source portMatch specific client or server portsDestination portMatch destination-side service portsICMP type/codeFilter echo requests, errors, etc.TCP flagsFilter packets by SYN, ACK, RST, FIN, combinationsPacket lengthFilter based on payload sizeDSCPQuality of service code pointFragmentMatch on packet fragmentation characteristicsSupported actionsGcore DDoS Protection supports the following Flowspec actions, which can be triggered when traffic matches a specific filter:ActionDescriptionTraffic-rate (0x8006)Throttle/rate limit traffic by byte-per-second rateredirectRedirect traffic to alternate location (e.g., scrubbing)traffic-markingApply DSCP marks for downstream classificationno-action (drop)Drop packets (rate-limit 0)Rule orderingRFC 5575 defines the implicit order of Flowspec rules. The crucial point is that more specific announcements take preference, not the order in which the rules are propagated.Gcore also respects Flowspec rule ordering per RFC 5575. More specific filters override broader ones. Future support for Flowspec v2 (with explicit ordering) is under consideration, pending vendor adoption.Blackholing and extended blackholing (eBH)Remote-triggered blackhole (RTBH) is a standardized protection method that the client manages via BGP by analyzing traffic, identifying the direction of the attack (i.e., the destination IP address). This method protects against volumetric attacks.Customers using Gcore IP Transit can trigger immediate blackholing for attacked prefixes via BGP, using the well-known blackhole community tag 65000:666. All traffic to that destination IP is dropped at Gcore’s edge.The list of supported BGP communities is available here.BGP extended blackholeExtended blackhole (eBH) allows for more granular blackholing that does not affect legitimate traffic. For customers unable to implement Flowspec directly, Gcore supports eBH. You announce target prefixes with pre-agreed BGP communities, and Gcore translates them into Flowspec mitigations.To configure this option, contact our NOC at noc@gcore.lu.Monitoring and limitationsGcore can support several logging transports, including mail and Slack.If the number of Flowspec prefixes exceeds the configured limit, Gcore DDoS Protection stops accepting new announcements, but BGP sessions and existing prefixes will stay active. Gcore will receive a notification that you reached the limit.How to activateActivation takes just two steps:Define rules on your edge router using Flowspec NLRI formatAnnounce rules via BGP to Gcore’s intermediate control planeThen, Gcore validates and propagates the filters to border routers. Filters are installed on edge devices and take effect immediately.If attack patterns are unknown, you’ll first need to detect anomalies using your existing monitoring stack, then define the appropriate Flowspec rules.Need help activating Flowspec? Get in touch via our 24/7 support channels and our experts will be glad to assist.Set up GRE and benefit from Flowspec today
Securing AI from the ground up: defense across the lifecycle
As more AI workloads shift to the edge for lower latency and localized processing, the attack surface expands. Defending a data center is old news. Now, you’re securing distributed training pipelines, mobile inference APIs, and storage environments that may operate independently of centralized infrastructure, especially in edge or federated learning contexts. Every stage introduces unique risks. Each one needs its own defenses.Let’s walk through the key security challenges across each phase of the AI lifecycle, and the hardening strategies that actually work.PhaseTop threatsHardening stepsTrainingData poisoning, leaksValidation, dataset integrity tracking, RBAC, adversarial trainingDevelopmentModel extraction, inversionRate limits, obfuscation, watermarking, penetration testingInferenceAdversarial inputs, spoofed accessInput filtering, endpoint auth, encryption, TEEsStorage and deploymentModel theft, tamperingEncrypted containers, signed builds, MFA, anomaly monitoringTraining: your model is only as good as its dataThe training phase sets the foundation. If the data going in is poisoned, biased, or tampered with, the model will learn all the wrong lessons and carry those flaws into production.Why it mattersData poisoning is subtle. You won’t see a red flag during training logs or a catastrophic failure at launch. These attacks don’t break training, they bend it.A poisoned model may appear functional, but behaves unpredictably, embeds logic triggers, or amplifies harmful bias. The impact is serious later in the AI workflow: compromised outputs, unexpected behavior, or regulatory non-compliance…not due to drift, but due to training-time manipulation.How to protect itValidate datasets with schema checks, label audits, and outlier detection.Version, sign, and hash all training data to verify integrity and trace changes.Apply RBAC and identity-aware proxies (like OPA or SPIFFE) to limit who can alter or inject data.Use adversarial training to improve model robustness against manipulated inputs.Development and testing: guard the logicOnce you’ve got a trained model, the next challenge is protecting the logic itself: what it knows and how it works. The goal here is to make attacks economically unfeasible.Why it mattersModels encode proprietary logic. When exposed via poorly secured APIs or unprotected inference endpoints, they’re vulnerable to:Model inversion: Extracting training dataExtraction: Reconstructing logicMembership inference: Revealing whether a datapoint was in trainingHow to protect itApply rate limits, logging, and anomaly detection to monitor usage patterns.Disable model export by default. Only enable with approval and logging.Use quantization, pruning, or graph obfuscation to reduce extractability.Explore output fingerprinting or watermarking to trace unauthorized use in high-value inference scenarios.Run white-box and black-box adversarial evaluations during testing.Integrate these security checks into your CI/CD pipeline as part of your MLOps workflow.Inference: real-time, real riskInference doesn’t get a free pass because it’s fast. Security needs to be just as real-time as the insights your AI delivers.Why it mattersAdversarial attacks exploit the way models generalize. A single pixel change or word swap can flip the classification.When inference powers fraud detection or autonomous systems, a small change can have a big impact.How to protect itSanitize input using JPEG compression, denoising, or frequency filtering.Train on adversarial examples to improve robustness.Enforce authentication and access control for all inference APIs—no open ports.Encrypt inference traffic with TLS. For added privacy, use trusted execution environments (TEEs).For highly sensitive cases, consider homomorphic encryption or SMPC—strong but compute-intensive solutions.Check out our free white paper on inference optimization.Storage and deployment: don’t let your model leakOnce your model’s trained and tested, you’ve still got to deploy and store it securely—often across multiple locations.Why it mattersUnsecured storage is a goldmine for attackers. With access to the model binary, they can reverse-engineer, clone, or rehost your IP.How to protect itStore models on encrypted volumes or within enclaves.Sign and verify builds before deployment.Enforce MFA, RBAC, and immutable logging on deployment pipelines.Monitor for anomalous access patterns—rate, volume, or source-based.Edge strategy: security that moves with your AIAs AI moves to the edge, centralized security breaks down. You need protection that operates as close to the data as your inference does.That’s why we at Gcore integrate protection into AI workflows from start to finish:WAAP and DDoS mitigation at edge nodes—not just centralized DCs.Encrypted transport (TLS 1.3) and in-node processing reduce exposure.Inline detection of API abuse and L7 attacks with auto-mitigation.180+ global PoPs to maintain consistency across regions.AI security is lifecycle securityNo single firewall, model tweak, or security plugin can secure AI workloads in isolation. You need defense in depth: layered, lifecycle-wide protections that work at the data layer, the API surface, and the edge.Ready to secure your AI stack from data to edge inference?Talk to our AI security experts
Subscribe to our newsletter
Get the latest industry trends, exclusive insights, and Gcore updates delivered straight to your inbox.