Artificial intelligence is transforming the way organizations work. Large language models and generative AI assistants help employees write emails, summarize meetings and make faster decisions. These tools promise to increase productivity, unlock innovation and reduce costs. However, with great power comes great responsibility. The same AI tools that accelerate your work can also expose sensitive data in unexpected ways. When a model has access to corporate documents, email threads and internal chats, a malicious prompt or misconfiguration can coax it into sharing information that was never meant to leave the organization. Because the user may not even know a model has been compromised, the leak can go undetected until it causes serious harm.
Cybersecurity and data protection leaders must recognize that AI is a new and different attack surface. The rules of traditional security still apply: protect data, limit access, and monitor usage. Yet AI brings new challenges. The logic of an AI assistant is hidden in a complex network of weights and training data. Attackers can trick the model into executing hidden instructions embedded in seemingly harmless content. Even trusted employees can inadvertently paste sensitive data into tools without realizing it will be stored or shared outside of company controls. It is not enough to adopt AI; organizations must secure it.
Large language model assistants are no longer experimental. Enterprises across industries have rushed to integrate generative AI into their workflows. Microsoft 365 Copilot is one of the most prominent examples. By mid 2024 more than ten thousand businesses had adopted Copilot. The assistant works across Outlook, Word, Excel, PowerPoint and Teams. Employees use it to draft content, generate reports, summarize conversations and search organizational knowledge. Adoption has been swift because Copilot and similar tools embed AI directly into familiar software. Without leaving the application, a worker can ask the AI to write a status update or analyze a spreadsheet.
Several factors drive this rapid growth. First, generative AI lowers the barrier to automation. Non technical users can ask natural language questions instead of writing code. Second, enterprises face competitive pressure. Leaders believe that companies leveraging AI will be more productive, so they encourage employees to experiment. Third, major technology vendors have built AI into their flagship products, making adoption almost unavoidable. Microsoft includes Copilot licenses in some enterprise subscriptions, meaning many companies already pay for it. Finally, early success stories fuel interest. Organizations report faster content production, improved customer service and more informed decisions when using AI.
However, adoption is outpacing risk management. Many organizations allowed broad access to AI tools without setting clear policies for usage, classification of data or integration with existing security controls. Employees use consumer chat bots with little oversight. They may input proprietary code or personal data into public models that record interactions. Without guidance, developers experiment with plug in architectures and retrieval augmented generation (RAG) systems that connect models to internal databases. This connectivity increases the risk of accidental exposure. According to the National Institute of Standards and Technology, prompt injection attacks — where adversaries provide malicious instructions that override a model’s intended behavior — have been flagged as a top emerging risk. NIST calls indirect prompt injection “generative AI greatest security flaw”. The Open Web Application Security Project (OWASP) ranks prompt injection as the number one threat to large language model applications.
AI adoption therefore amplifies the need for security leaders to understand how these tools work and how they can be abused. While organizations want to empower their workforce, they must also protect data and comply with regulations. Achieving both requires a proactive approach.
When employees interact with AI assistants, they often assume the tool is as safe as any corporate software. After all, the assistant appears inside trusted applications. Yet AI tools introduce new paths for data to escape. Understanding these mechanisms is essential to prevent breaches.
AI models produce responses based on input prompts and, in enterprise contexts, additional context retrieved from internal sources. Attackers have realized that if they can influence the prompt or the content the model reads, they can manipulate its behavior. A prompt injection attack embeds malicious instructions into a document or message. When the model reads the content, it executes the hidden commands. This can cause the model to ignore guardrails, leak sensitive information or perform actions outside its intended scope. Because the model follows the instructions in its context, the user may not realize anything is wrong until the response contains unauthorized data.
Modern AI tools often act as agents, meaning they can call external APIs, query databases or send emails to accomplish tasks. This connectivity increases utility but expands the attack surface. If an attacker tricks the model into using its privileges, they can access data or trigger actions. For example, if a chatbot has permission to query a customer relationship management system, an injection could request all customer records. The model might output the data or send it to an attacker controlled endpoint. Without proper restrictions and monitoring, such misuse may not be detected.
Traditional software development includes input validation to prevent injection attacks. AI systems require similar protections but the complexity is higher. Models can interpret instructions embedded in images, code blocks or markup languages. If an organization does not filter these inputs or sanitize outputs, hidden instructions may slip through. In the EchoLeak case, the attack used reference style Markdown links that bypassed link redaction. Because the model summarization function automatically included the link, the data exfiltration occurred without user interaction.
In some organizations, employees use consumer AI tools for convenience. They paste sensitive data into public chat bots or use open source models on personal devices. These unsanctioned tools may store and reuse the data, creating a new path for leaks. Moreover, employees may not realize that their actions could violate regulatory or contractual obligations. Without a clear policy and training, shadow AI becomes a blind spot.
AI interactions often remain invisible to standard security monitoring. Logs may not capture the content of prompts and responses, or the system may not label data as sensitive. If a prompt injection occurs, there is little way to detect it. Many organizations also lack formal governance structures to determine who owns AI tools, who approves use cases and how to respond to incidents. Without defined processes, the organization is slow to react to emerging threats.
These risk factors underscore the need for new security practices tailored to AI. Because the models are dynamic and can be influenced by attackers, they must be treated like active code. The following case study illustrates how quickly a vulnerability can be weaponized.
In June 2025, researchers at Aim Security disclosed EchoLeak, the first known zero click prompt injection vulnerability to cause real data exfiltration in a production AI system. EchoLeak targeted Microsoft 365 Copilot and exploited the way it processed Outlook emails. The attack required no user interaction. By sending a specially crafted email, the attacker could trick Copilot into accessing internal files and sending them to an external server. Microsoft assigned the vulnerability the identifier CVE 2025 32711 and issued emergency patches to fix it. Understanding how the exploit worked provides valuable lessons.
EchoLeak is a wake up call. It shows that AI systems can be exploited in ways that bypass both traditional security controls and new AI specific defenses. Security leaders must assume that these kinds of vulnerabilities will continue to emerge. The broader risk landscape includes more than just prompt injection. Understanding other attack vectors is essential to prepare for what is coming.
The EchoLeak incident is not isolated. Researchers and attackers are exploring many ways to abuse AI systems. Below are several categories of risks that organizations should consider.
AI models learn from training data. If an attacker can insert malicious data into the training set, they can influence the model’s behavior. For example, a competitor might feed negative reviews about your products into a sentiment analysis model. When your model trains on the poisoned data, it may produce biased results. Data poisoning can also insert backdoors into the model. During inference, a specific trigger could cause the model to output sensitive information. Because training data often comes from multiple sources, verifying its integrity is challenging.
Attackers may attempt to reconstruct sensitive training data by querying the model. If the model is exposed via an API, repeated queries could reveal information about individuals in the training set. Membership inference attacks determine whether a specific data record was included in training. These attacks can violate privacy regulations and disclose personal or proprietary data.
Many AI applications rely on third party components: open source models, plug ins and APIs. A vulnerability in any component can compromise the entire system. For example, a plug in that fetches data from external sources might not handle input validation properly, allowing cross site scripting or injection attacks. Organizations need to perform due diligence on third party AI tools and manage dependencies.
Models like Copilot may have broad access to corporate documents, emails and chat histories. Without proper scoping, they can access more data than necessary. If a vulnerability exposes the context, a large amount of sensitive data is at risk. Security teams should enforce the principle of least privilege for AI models, ensuring they only retrieve what they need for a specific task.
As mentioned earlier, employees sometimes use unapproved AI tools. They may also build their own prototypes using open source models or call external APIs without security review. These shadow projects can become gateways for attackers. Without centralized oversight, it is hard to track what data these tools process and where it goes. Creating a catalog of AI tools and requiring security sign off for new projects helps mitigate this risk.
AI applications must comply with privacy regulations such as GDPR or HIPAA. If a model processes personal data, the organization needs consent, transparency and the ability to erase data on request. Generative AI complicates this because it can memorize or regenerate sensitive content. Legal teams need to work closely with technology and security teams to define acceptable use and retention policies for AI interactions.
Organizations that understand these risks can take steps to mitigate them. The next section outlines a practical approach to building an AI security program.
Securing AI is not a one time project; it requires ongoing governance, technical controls and cultural change. Below are actionable steps that CISOs and security leaders can take to build a proactive AI security program.
Start by cataloging all AI applications and services in use. This includes enterprise tools like Copilot, customer facing chatbots, AI embedded in products and experimental prototypes. For each application, document the data sources it uses, the actions it can perform and the type of outputs it generates. Classify the sensitivity of the data involved. This inventory helps you understand your AI footprint and identify high risk areas.
Work closely with AI vendors such as Microsoft to understand their security measures and update schedules. Subscribe to security advisories and patch regularly. Participate in industry forums to share best practices and stay informed about emerging threats. When vendors release new features, evaluate their security implications before enabling them.
Prepare for the possibility of an AI related incident. Develop procedures for detecting, containing and responding to prompt injection, data exfiltration or model misuse. Identify the stakeholders who must be involved, including IT, legal, communications and executive leadership. Practice tabletop exercises to test the response plan. Ensure that you can quickly revoke access for compromised models, isolate affected systems and notify users.
Treat AI security as part of your overall cyber risk management program. Include AI risks in risk registers, report them to the board and incorporate them into business continuity plans. Align AI security controls with existing frameworks such as NIST or ISO. This integration helps ensure that AI does not remain a silo but becomes a standard part of security conversations.
Building a comprehensive AI security program takes time. Partnering with experts who understand both AI technology and cybersecurity can accelerate progress. Netrix Global offers such expertise.
Netrix Global is an engineering led IT consulting and managed services provider with decades of experience across cybersecurity, digital workplace, cloud infrastructure and data intelligence. We act as an extension of our clients’ teams, providing end to end services from strategy to implementation. Our deep bench of security experts operates twenty four hours a day, seven days a week, delivering managed detection and response, vulnerability management and incident response. Through our long term partnership with Microsoft, we participate in the Microsoft Intelligent Security Association (MISA) and deliver specialized workshops and managed services.
Netrix Global offers specialized services for securing generative AI. We conduct threat modeling to identify how AI tools might be attacked, including prompt injection, model poisoning, supply chain vulnerabilities and context overexposure. We design technical controls such as input validation, output filtering, identity management and access controls. We also assist with policy development. Our consultants help clients draft acceptable use policies, define approved tools and create processes for risk assessment. As part of our membership in MISA, we leverage the latest Microsoft security innovations to protect tools like Copilot. We collaborate with Microsoft to understand new features, test their security and implement best practices for deployment.
Artificial intelligence presents a transformative opportunity for businesses. It boosts productivity, enhances decision making and fuels innovation. Yet the same tools can expose sensitive data if not properly secured. The EchoLeak zero click exploit proved that AI systems can be manipulated to leak confidential information through hidden instructions. The rapid adoption of tools like Copilot and the ranking of prompt injection as the top risk for large language models demonstrate that the threat is real and immediate. CISOs and IT security leaders must move beyond curiosity to action.
Start by understanding your AI inventory and educating your teams about risks. Develop governance structures that align AI with business goals while enforcing security and compliance. Implement technical controls that limit the model’s access, validate inputs and monitor outputs. Test your systems against adversarial scenarios and prepare response plans for AI incidents. Partner with experts like Netrix Global who offer deep experience across cybersecurity, data intelligence and AI governance. We act as an extension of your team, providing twenty four hour security coverage and leveraging a decades long partnership with Microsoft to bring the latest solutions.
The journey to secure AI is ongoing. By taking proactive steps now, you can harness the benefits of AI while protecting your organization’s most valuable assets. Netrix Global is ready to guide you. Contact us to schedule an AI security readiness assessment and learn how we can help you build an AI program that is both innovative and safe.
Hidden data leaks occur when AI systems expose sensitive information without the user realizing it. This can happen through prompt injection, where malicious instructions cause the model to reveal data, or through misconfigured context retrieval that allows the model to access more information than necessary. The leaks are “hidden” because the user might see a harmless response while the underlying system sends data to an attacker or saves it in an unsecured location. EchoLeak is an example of a hidden leak triggered by a malicious email.
EchoLeak exploited several features in Microsoft Copilot to achieve zero click data exfiltration. The attacker sent an email with hidden instructions embedded in a reference style link. Copilot’s prompt injection classifier did not detect the malicious instructions. When Copilot summarized the email, it included a link that pointed to a Microsoft Teams domain, which then forwarded the request to the attacker’s server. Because the request came from a trusted domain, security controls did not block it. The user did not need to click anything; the data was exfiltrated automatically.
AI vendors are developing safeguards such as classifiers to detect malicious inputs and output filters to sanitize responses. In Copilot, the XPIA classifier is designed to detect injection attempts. However, EchoLeak demonstrated that attackers can craft payloads that bypass such classifiers. AI security is evolving, and defenses may lag behind new attack techniques. Organizations should not rely solely on vendor protections. They need to implement additional controls like input validation, context partitioning and network filtering.
Start by conducting a risk assessment and inventory of all AI tools in use. Implement policies that specify what data can be shared with AI and which tools are authorized. Apply least privilege access to limit the data each model can view. Use filters to detect and remove malicious content. Monitor AI interactions and log prompts and outputs for anomaly detection. Train employees to use AI safely and report suspicious behavior. Partner with experts like Netrix Global to design and implement these controls.
Netrix Global combines decades of experience across cybersecurity, cloud, and data intelligence. We act as an extension of your team and operate a twenty four hour security operations center. We are a strategic partner of Microsoft and part of the Microsoft Intelligent Security Association, giving us early insight into emerging threats and defenses. Our services include AI readiness assessments, data platform design, AI security and governance, custom agent development, and managed security operations. With Netrix Global, you gain a trusted advisor to help you build a secure and resilient AI program.
