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What Is Zero Trust Security?

  • By Gcore
  • October 11, 2023
  • 12 min read
What Is Zero Trust Security?

Zero Trust is a security approach that assumes no one inside or outside the network can be automatically trusted, so verification is required for every user and device trying to access resources in an organization, every time they request access. In this article, we’ll explore what zero trust is, why and how you should implement it, what challenges to look out for, and best practices.

What Is Zero Trust?

Zero trust, also known as perimeterless security, is a security model that assumes that an organization is constantly at risk from internal and external factors. There’s no official standard or certifying body for zero trust; instead, it’s a conceptual framework, a way of thinking about security. It enables organizations to build and strengthen defenses around the mantra “never trust, always verify,” meaning that every application, endpoint, and user in an enterprise’s IT environment is treated as a potential threat.

As a result, any user or device attempting to access a digital resource must undergo authentication processes to prove legitimacy every time it seeks access to the organization’s network or assets. Gaining access to IT assets once does not mean that a user or device is authorized permanently. Authentication will need to occur anew every single time. For example, if you log into your work email account on Monday morning using two-factor authentication, on Tuesday you’ll have to do the same thing again to access your emails.

Zero trust is piquing the interest of organizations of all sizes across all sectors because of its stringent attitude towards digital security, increasingly important in the age of remote work. The global market is forecasted to grow at a compound annual rate of 17.3% from 2022 to reach $60.7 billion by 2027. According to Gartner, 10% of large enterprises will have a mature zero trust program by 2026.

What Defines a Zero Trust Infrastructure?

As mentioned, there are no official standards for zero trust security. A zero trust security model is a combination of multiple criteria, often referred to as the pillars of zero trust because they are the foundation upon which a zero trust model is implemented.

Identity

Establishing and managing user and system identities is the foundational layer of zero trust security. In a zero trust model, it’s vital to provision and deprovision digital identities optimally. Key tools for protecting identities include:

  • Access control lists (ACLs)
  • Identity and access management (IAM)
  • Single sign-on (SSO)
  • Multi-factor authentication (MFA,) including:
    • One-time passwords (OTPs)
    • Email authentication
    • Biometrics

The principle of least privilege is essential to this pillar, and ensures that users access only the IT resources necessary for their tasks. For example, a regular employee might have access to only the files and software relevant to their job function, while an IT administrator would have broader access to manage system settings and security protocols. Any non-essential privileges should be revoked; in other words, if an employee changes roles and no longer requires access to a specific database, that access should be immediately revoked to minimize security risks.

Context-based access restrictions are defined by criteria such as the user’s location, endpoint type, and access time, determining the extent of access to resources. For instance, a user accessing the system from a company-approved device within the office might have full access to resources, while the same user attempting to access the system from a public Wi-Fi hotspot might find themselves with restricted capabilities. Similarly, access could be time-sensitive, allowing certain actions only during business hours.

Device

With the proliferation of smart devices and IoT, ensuring their security and integrity is an indispensable aspect of a zero trust approach. All enterprise endpoints, BYOD devices, and IoT machines that are connected to company networks should be in a centralized inventory and management system to ensure real-time monitoring and ad-hoc authentication. Regular assessment of device hardware and timely software patching are vital in a zero trust environment.

Network

Given that a network acts as the circulatory system for data transmission, ensuring its security and integrity becomes paramount in a zero trust framework. Network microsegmentation is one important part of this, and entails dividing the network into optimized segments for isolated monitoring and traffic control. Encryption is also essential, ensuring that data in transit is inaccessible to unauthorized users.

Data

Data is a paramount asset, with the prevention of breaches being the primary goal of zero trust. Protecting data involves understanding its entire lifecycle, from collection to disposal, and employing strategies like tokenization, masking, and encryption. For instance, a healthcare provider might collect patient data, then tokenize the Social Security numbers and encrypt medical records. This information can then be stored in a secure cloud environment, accessible only through multi-factor authentication, ensuring that even if a breach occurs, the sensitive data remains unreadable.

Thorough visibility into IT infrastructure is key—for example, via monitoring tools like Security Information and Event Management (SIEM.) These tools can help organizations track suspicious activity in real-time. Consider a retail business that uses SIEM to monitor traffic to its online store. If a series of failed login attempts from a foreign IP address are detected, the SIEM system can flag it for immediate review, possibly preventing unauthorized access to customer data. This aids in vulnerability identification, breach mitigation, and precise incident remediation.

Applications and Workloads

This pillar encompasses application workloads, virtual machines, and containers. These components serve as vital communication points within IT infrastructures. Such components in a zero trust model are assumed hazardous and are continually monitored, tested, authenticated, and authorized.

Furthermore, automation ensures precision in this process. For instance, an automated workflow could routinely check that all virtual machines are running the latest security patches and flag any that aren’t for immediate attention. Orchestration allows for the efficient coordination of different tools, technologies, and practices. In a real-world scenario, orchestration could mean that as soon as a vulnerability is detected in one part of the system, countermeasures like isolating affected components can be automatically initiated, while simultaneously alerting the security team.

Why Should You Implement Zero Trust?

There are quantifiable advantages to implementing zero trust. Organizations with a zero trust security model saved close to $1 million in data breach costs compared to those with traditional security models.

Protect Legacy Infrastructure

Though legacy infrastructure is often seen as a security problem by companies, it’s not always financially realistic to replace it all at once. The healthcare and banking sectors still heavily rely on outdated and highly patched applications like databases and payment systems. These critical legacy systems can’t be replaced overnight without compromising business continuity. 

Zero trust can help to protect and maximize the use of these vulnerable legacy systems before and during digital transformation initiatives. For example, consider a large healthcare provider that still relies on an older electronic health records (EHR) system. An immediate transition to a new system could disrupt patient care and introduce a variety of complications. By implementing a zero-trust approach, the healthcare provider can add an extra layer of security to this legacy system. Any user or system trying to access the EHR must undergo stringent authentication and authorization checks. Even within the network, the system is continuously monitored for unusual activity or vulnerabilities. This allows the healthcare organization to continue operating without disruption while gradually transitioning to more modern infrastructure.

Defend Against Phishing

Phishing campaigns are organized efforts by threat actors to extract sensitive personal information from victims by pretending to be legitimate requests. The different types of phishing include spear fishing, which targets an individual rather than a group, whaling, which targets highly-ranked personnel like C-suite executives, email phishing, which tricks a victim into providing sensitive information, and pharming, which redirects victims to illegitimate websites disguised as familiar websites.

Zero trust features like MFA, Mobile Device Management (MDM), micro-segmentation, and remote access policies can help enterprises defend against phishing campaigns by adding multiple layers of security that validate the identity of users and the health of their devices before granting access to the network. These measures limit the potential impact of a successful phishing attack by requiring additional credentials or device verification, thus making it more challenging for threat actors to exploit stolen information for unauthorized access.

Enable Safe Global Collaboration

An increasing number of enterprises are entering new markets and working with foreign entities. This means that more servers, privileged digital identities, and endpoints will be added and interconnected within an enterprise IT environment.

Zero trust can help ensure safe, compliant, and productive communication by implementing stringent access controls and continuous monitoring to verify the identity and trustworthiness of both users and devices. This minimizes the attack surface and reduces the risk of unauthorized access, even within a complex, multinational IT environment. By employing principles like least-privilege access and real-time verification, zero trust ensures that only authenticated and authorized entities can access sensitive information.

Manage Third-Party Access Risks

Businesses increasingly rely on third-party applications and add-ons to enhance their IT environments. However, third-party vulnerabilities accounted for 13% of data breaches in 2022 and remain a significant threat. Examples of vulnerable third-party applications include web browsers like Chrome and Safari, communication and collaboration apps like Zoom and Microsoft Teams, and a range of analytics tools and plug-ins.

Zero trust can ensure that third-party entities get only the bare minimum access to company networks to stay effective by implementing least-privilege access controls, real-time monitoring, and multi-factor authentication for any external software or services. This means third-party applications are only given the permissions they absolutely need to function, and their activities within the network are closely monitored to detect any anomalous or suspicious behavior. 

Encompass Distributed IT Infrastructures

Since zero trust is bound by context-based logic and policies, it can easily encompass distributed and scaling IT infrastructures. A distributed cloud model features numerous cloud infrastructures and services operating across IT environments, including on-premises data centers, public clouds, and third-party data centers. Distributed cloud models are typically controlled from a single centralized console.

With zero trust, companies can confidently grow their multicloud infrastructures knowing that their security program can protect rapidly-increasing identities, devices, networks, data, applications, and workloads. For example, a multinational retailer with multiple e-commerce platforms across different clouds can use zero trust to enforce strict access controls and continuous monitoring. This ensures all parts of their complex environment—public clouds, on-premises data centers, and third-party services—are secure, allowing for safe and scalable growth.

Prevent Malware

Malware is any software that’s designed with malicious intent. Undetected malware can cost companies millions in damages. The most common types of malware are ransomware, which locks a victim’s access rights until a ransom is paid, spyware, which secretly logs information about a victim’s digital activities, and Trojans, which camouflage as legitimate software to hijack a victim’s system.

Zero trust ensures that malware is detected and remediated in real-time before it can cause any lasting damage by enforcing strict access controls, continuous monitoring, and automated response protocols. In a zero trust environment, all network traffic, including that originating from inside the organization, is considered potentially risky and is closely scrutinized. Files and software are regularly scanned for malicious signatures, and users are required to go through multi-factor authentication before gaining access to network resources. Any deviation from established behavior patterns triggers automatic response mechanisms, such as isolating affected endpoints or revoking access rights, thereby containing the spread of malware and facilitating rapid remediation.

Facilitate Digital Transformation

According to Gartner, 89% of board directors claim that digital transformation is fundamental to their growth strategies, with 35% already having achieved or being on their way to doing so. Digital transformation can’t be achieved unless the challenges associated with the above points are mitigated via zero trust security.

Zero trust ensures that digital-centric growth strategies are secure and successful by bringing a holistic and strict attitude to security to digitally minded companies. For instance, a media company transitioning from print to digital can use zero trust to securely manage increased online traffic and protect digital assets. By implementing stringent access controls and ongoing monitoring, the company can focus on its digital strategy without worrying about security breaches.

Challenges of Zero Trust Implementation

While zero trust implementation has obvious benefits, it’s not a simple concept to apply in practice for the following reasons:

  • Lack of expert guidance: Zero trust implementation can be a highly challenging and technical process. Businesses often struggle to transition from older security models to zero trust without the help of experts, which may become a financial burden.
  • Implications on productivity: The objective of zero trust is to streamline access to critical IT resources by authenticated users. However, during implementation, employees may struggle to access resources and navigate a changing IT environment, and this can potentially affect productivity.
  • Legacy IT infrastructure: Legacy IT infrastructure may not be easy to integrate into a zero trust architecture, making it a hurdle to overcome during the implementation process.
  • Buy-in from key stakeholders: The implementation of a zero trust security model needs the buy-in of more than just IT and security teams. All key stakeholders, including the board of directors and C-suite executives, need to have confidence in zero trust and understand the organization-wide advantages it can provide.
  • Highly technical process: While zero trust is more of a framework than a technology, its implementation is still a highly technical process that can be time consuming and resource intensive.
  • High costs: The long-term benefits of zero trust include cost savings via optimized budgets and money saved from preventing data breaches. However, the implementation process can be expensive, depending on the size of the organization and the scope of the IT environments. The long-term cost-savings typically outweigh the short-term expenses, but require upfront capital investments.
  • Lack of holistic strategy: Even the most meticulous execution can yield poor results if zero trust implementation isn’t bound by a holistic strategy. The success of zero trust implementation relies heavily on clarity and intent.

Best Practices When Implementing Zero Trust

In order to experience the full benefits of zero trust and overcome its potential implementation challenges, adhere to the following best practices.

Prioritize Network Segmentation

Businesses should divide their network into small and isolated microsegments. Network segmentation can streamline workloads, enable smooth traffic flows, and ensure that security incidents are isolated and easily solvable. 

To divide your network into isolated microsegments, begin by conducting an inventory of your existing IT assets, such as servers, databases, and workstations. Use network mapping tools to visualize data traffic flows between these assets. Once you have this data, consult with your IT and security teams to identify potential risk points and determine how to segregate assets based on factors like their function, the sensitivity of their data, and their exposure to security risks.

Use access control lists (ACLs) to specify which users or system processes are granted access to each microsegment. Configure firewalls to monitor and control incoming and outgoing network traffic based on an organization’s previously defined security policies.

Implement software-defined perimeters (SDPs) to provide a more flexible and adaptable network security framework. By combining these elements, you can create a segmented network that not only enhances performance and traffic management but also bolsters your security posture.

Encrypt Data

Data is the main target for threat actors. Therefore, companies should encrypt all data, both at rest and in transit, so that only authorized and authenticated users can access and read it. Data encryption transforms plaintext into ciphertext, which can only be deciphered with a specific key. The two primary kinds of data encryption, symmetric and asymmetric, depend on whether the key for hiding and unveiling data is the same. 

Businesses should ideally use a mix of symmetric encryption styles and asymmetric encryption styles. Utilizing both symmetric and asymmetric encryption methods allows businesses to balance speed, security, and compliance requirements. Symmetric encryption is faster and less resource intensive, making it ideal for encrypting large data sets. However, it uses a single key for both encryption and decryption, posing a risk if the key is compromised. Asymmetric encryption uses a public key for encryption and a private key for decryption, eliminating the key distribution problem inherent in symmetric encryption and adding functionalities like digital signatures. By combining both encryption styles, businesses can achieve a layered security approach that meets regulatory standards and is resilient against diverse cyber threats.

Conduct Regular Red Teaming

Companies should regularly pretend to hack into their own systems to see if their security measures are working well. This practice, known as “red teaming,” can be done by their own tech staff or by hiring outside experts. The goal is to find any weak points in their security. They should check how a hacker could get in, what damage they could do, how far they could move within the system, and what the company’s ability is to spot and stop the attack as it happens. This helps make sure the company’s zero trust approach to cybersecurity is effective.

Elevate Endpoint Security

Hackers are more frequently targeting both company-owned and personal devices that connect to business networks. So, it’s important for businesses to focus on making these devices—known as endpoints—as secure as possible. To do this, companies should keep a detailed list of all such devices, make sure they meet certain security standards before they can connect to the network, and control who can access what information on a given device.

They should also use special tools to watch for signs of hacking attempts on these devices and take action if they detect anything suspicious like antivirus software, intrusion detection systems (IDS), and endpoint detection and response (EDR) solutions. These tools provide real-time analysis and help in identifying, managing, and mitigating risks effectively. For example, EDR software continuously monitors and collects data from endpoints to detect unusual patterns or behaviors that could indicate a security threat. If a potential threat is detected, the EDR software can automatically isolate the affected device from the network, preventing the spread of malware and providing time to investigate and remedy the issue.

Develop Remediation Plans

The “assume breach” mentality states that data breaches are inevitable. Therefore, companies should always be ready with updated and tested remediation plans. Businesses must define acceptable and unacceptable cyber risks. Acceptable risk is typically low-priority vulnerabilities that do not affect business-critical processes. Remediation plans need to center around unacceptable risks and critical vulnerabilities. It’s also important to plan which teams, stakeholders, and vendors need to be notified and involved in the event of a security breach.

Businesses must define which remediation processes will be automated and which will need manual intervention. Start by listing out all the remediation steps typically taken after identifying a cybersecurity incident. For each step, decide whether it can be automated or if it requires human judgment and action. For example, isolating a compromised system from the network could be automated, but deciding the next course of action might need manual review. Document these decisions in a remediation playbook so everyone on the team knows what to do during a security event.

Most importantly, remediation includes reporting on security incidents and using those insights to strengthen the next iteration of the zero trust architecture. After an incident has been resolved, gather all relevant data and create a detailed report. This should include what the vulnerability was, how it was exploited, what actions were taken to remedy it, and how effective those actions were. Share this report with key stakeholders, including IT teams, management, and any third-party vendors involved. Use the findings from this report to update your zero trust architecture—this could mean revising access controls, updating software, or improving monitoring capabilities. Make sure to also update your remediation playbook based on what you’ve learned.

Employee Orientation

Every employee needs to be well-versed in the zero trust security approach, as it’s essential to the company’s overall cybersecurity. Training sessions should be mandatory, highlighting key concepts such as “least privilege,” which means only giving employees the minimum levels of access—or permissions—they need to accomplish their tasks. This should be more than a one-time orientation; it must be integrated into ongoing HR policies and employee development programs. Staff must fully grasp how their daily work activities can impact the company’s security. While zero trust is built on various technologies and tools, its success relies on the consistent, responsible actions of each and every team member. Therefore, instilling a culture of continuous security awareness is essential.

Conclusion

Zero trust security is vital to protect your most valuable data and IT assets across multicloud environments. With the knowledge you’ve gained from this article, you can navigate the complexities of zero trust, ensuring a robust and effective security implementation. 

Interested in reaping the benefits of zero trust? Explore Gcore to see how world-class DDoS protection, web application security, and bot protection can transform your business and strengthen an existing, in-progress, or forthcoming zero trust security architecture.

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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

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 cover image

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

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