AI Data Security

The rate at which companies are adopting enterprise AI systems is currently outpacing the development of the appropriate security infrastructure. Companies are deploying large language models (LLMs) and generative AI tools into production workflows at unprecedented speed. Yet most lack visibility into where sensitive data flows or how those tools may be exposing it.

As reported in Proofpoint’s Voice of the CISO Report, 85% of security professionals list data loss as their top concern when deploying AI systems. The anxiety surrounding this issue is justified, given that AI systems need access to vast amounts of data to train and operate. Limited control over the system means they can inadvertently reveal a customer’s personal identifiable information, an organisation’s intellectual property, or regulated information.

AI data security comprises policies, technologies, and methods for protecting data throughout an artificial intelligence system’s life cycle. This includes keeping training datasets safe, ensuring that real-time input/output data to the inference engine is protected, and ensuring that only authorised users can access model parameters and telemetry. AI data security encompasses both traditional data protection methods and AI-specific approaches, such as prompt filtering, output sanitisation, and model behaviour monitoring.

AI data security differs from traditional data security in its unique vulnerabilities. An AI model may learn and reproduce the sensitive training data it was trained on. Additionally, an AI model’s prompts may be unchecked to elicit confidential information, and its outputs may include proprietary insights and/or reveal patterns contained in that data. Every stage of an AI system’s life cycle has its own attack surface and data exposure point(s).

For example, an AI application may draw upon customer databases, process real-time user input, log interaction data, and store model outputs across a distributed infrastructure. Security teams need visibility into each AI system’s data flow to apply the necessary controls.

In turn, effective AI data protection requires a layered approach to address the risks associated with data, models, and deployments.

The first step to protecting data is to identify all the various types as they move through an organisation’s AI life cycle. The different types of data (training, validation/test, inference/prompt, output/telemetry) present unique risk vectors. Therefore, security teams must map and control data.

• Training data is what an AI model is built on. It can be structured datasets, unstructured documents, customer records, or transaction logs. The AI model learns and reproduces parts of the training data that contain private information. When using third-party models or fine-tuning pre-trained systems, many companies don’t realise how much data is going into their training pipeline.
• Validation and test data are used to evaluate an AI model’s performance during development. This data usually mirrors production data and may contain the same confidential information. A breach of validation data could reveal business logic, customer behaviour patterns, operational metrics, etc., that some security teams may mistakenly classify as less sensitive than production data.
• Inference and prompt data are real-time inputs users are submitting to AI systems (e.g., questions asked, documents uploaded, context provided). All of these items can contain intellectual property, personally identifiable information, and strategic business intelligence. Without proper filtering, the data submitted to an AI system can be logged, cached, or sent to external model providers.
• Outputs and telemetry complete the data landscape. AI-generated responses can reveal training data or leaked private information through poorly protected output layers. System logs keep full copies of prompts, outputs, and information about how users interact with the system. Because they show how AI was used, security teams need to treat both output and telemetry data as sensitive.
• Embeddings and context vectors are numerical representations that AI models use internally to create meaningful relationships between words and concepts. Embeddings may look abstract, but they retain the meaning of words and can be used to infer the original inputs. Any organisation that stores embeddings for retrieval-augmented generation needs to protect these vectors as well as they would protect source data.

Recent studies show that 64% of businesses don’t fully understand their AI risks, and 69% say that AI-powered data leaks are their biggest security worry. The challenge looks different for each role in the company.

• As frameworks like the EU AI Act call for transparency in how models are governed and data is handled, CISOs are under increasing regulatory pressure. They need to show how risky all their AI deployments are while also explaining to boards why they need to spend money on security, even though they see AI as a way to stay ahead of the competition.
• SecOps and incident response teams struggle to see the whole picture. They need to find out whether insiders are misusing AI, monitor how shadow AI is being used, and retain telemetry that records all AI interactions without proper logging to investigate incidents.
• CIOs and CTOs find a balance between giving people the tools they need and keeping an eye on the big picture. They need to update their tech stacks to support AI innovation while still retaining control over which APIs, models, and services can access company data.
• Compliance teams have to deal with changing privacy rules. They need to check how AI systems handle personal data and demonstrate that they follow the rules everywhere, even when those rules differ.
• Messaging and IT admins enforce policies while the system is running by controlling which plug-ins employees can use and by stopping risky prompts before they reach production models.

“As data security methods struggle to keep up with these technological strides, the responsibility falls heavily on data security specialists,” says Vamsi Koduru, Staff Product Manager at Proofpoint. “They must ensure their organisations remain in compliance with ever-evolving regulations, even as AI-driven transformations continually reshape the data landscape.”

Autonomous AI projects, like AI agents, have become mainstream in enterprise operations, whether for customer service, strategic decision-making, etc. This shift has created pressure to integrate AI applications with operational data rather than synthetic data, which inherently increases risk when control environments lag application adoption. The threat of shadow AI is also growing as end users experiment with non-approved solutions and test their own use cases, creating disjointed and inconsistent governance and security postures across the organisation.

Collaboration platforms now ship with embedded AI features and optional extensions that provide additional AI capabilities within live conversation, shared documents, and knowledge management cases. This increases the number of entry/exit points for sensitive data through the AI system. The inclusion of third-party tools and ecosystem integrations provides additional unmanaged and possibly insecure pathways for adversaries to introduce malicious code into the AI application.

R&D and engineering organisations rely heavily on AI to automate code reviews and accelerate experimental cycles. However, this could expose proprietary algorithmic approaches or create potential vulnerabilities in external services if proper controls aren’t in place to contain the results. Adversaries use AI to significantly reduce the time required for reconnaissance and to create more convincing social engineering attacks, resulting in accelerated response timeframes.

As regulatory requirements evolve and mature, auditors will increasingly request evidence on AI-based logging and data retention, which will encourage organisations to develop formalised AI data security programmes.

AI applications are being attacked from both inside and outside by bad actors and by poor internal design and operation.

• Data leakage during training or fine-tuning: Model training uses your sensitive company data, which may output confidential information and make it available to unauthorised users.
• Data exfiltration via AI tools: When employees use AI tools (like ChatGPT) to assist with their work, they may unknowingly send your company’s sensitive information to a provider that retains input from previous interactions for model development.
• Identity and OAuth credential misuse: The compromise of an OAuth token or service account provides a single point of entry for the attacker to gain access to all of your company’s integrated data through your AI application.
• Prompt injection and jailbreak attacks: Bad actors can develop inputs specifically designed to bypass your system’s guardrails and manipulate your model to perform some action you didn’t authorise and/or reveal restricted information.
• Adversarial and data poisoning attacks: Attackers may attempt to inject malicious data into your training sets to either degrade performance, introduce bias into your model, or create backdoors for future exploitation.
• Model inversion and membership inference: More sophisticated attackers can query your model to determine if they can rebuild your training data and/or whether a particular individual’s data was used in the model’s training process.
• Embeddings and vector database leakage: Vector embeddings may contain enough of your sensitive, proprietary information that attackers can recreate the original proprietary document(s) or even the original customer data.
• Third-party and plug-in supply chain vulnerabilities: External plugins, API’s, and pre-trained models add additional dependencies on third-party security best practices that may be suboptimal.
• Insider threats from negligent, malicious, or compromised users: Authorised users may intentionally or unintentionally expose data, exfiltrate information, or have their user accounts compromised by external attackers.
• Enforcement gaps and limited visibility: Traditional security tools don’t have insight into the new risks presented by AI, such as prompt content and model behaviour, making it difficult to detect these issues and enforce appropriate policies.

Effective AI security requires a layered approach to protect data, identity, runtime behaviour, and organisational processes. To reduce risk in each stage of the AI life cycle, use these strategies:

• Data classification and tagging: Label all sensitive data as it enters an organisation (data ingestion) to automate how it is handled before it ever reaches any AI systems.
• Identity and access management with least privilege: Only allow permissions that necessitate access to specific data sources and to perform only those actions that are needed to complete a specific task.
• Data lineage and provenance tracking: Document all data sources used, all transformations applied to the data, and all training inputs provided to the model so that audits can be conducted and incident investigations supported.
• Policy enforcement frameworks: Use automated blocking systems to prevent the transmission of prohibited data types into AI workflows, based on classification rules.
• Telemetry and activity monitoring: Record all interactions with AI systems, including prompts, outputs, and data access patterns for anomaly detection.
• Model runtime controls and guardrails: Use input validation, output filtering, and behavioural constraints to prevent malicious manipulation or unintended disclosure of information.
• DLP integration for AI channels: Apply existing data loss prevention tools to monitor usage of AI tools to ensure that no sensitive data is transmitted to external providers.
• Plug-in and connector governance: Develop approval workflows and security reviews before enabling third-party AI extensions or integrations.
• Secure development and research workflows: Ensure experimental AI environments are isolated from production data and that code review happens before model deployment.
• User training and awareness programmes: Provide employees with regular AI-specific security training and education on AI risks, approved AI tools, and safe data-handling practices.

Maintain a Comprehensive AI Asset Inventory

Keep a record of all the AI tools, models, and platforms that the company uses, even the ones that IT doesn’t know about. Regular discovery scans and user surveys help find prohibited tools that typically poke holes in security.

Establish Data Classification Policies for AI Usage

Based on data sensitivity and regulatory compliance requirements, decide which types of data AI systems can process and which ones they can’t. Document these rules in a way that’s easy for non-technical users to understand and apply in their daily work.

Implement Strong Identity and Authentication Controls

Require multifactor authentication (MFA) for all users across a given AI platform, and regularly check OAuth grants to eliminate any excessive permissions. AI apps should automatically change the passwords for their service accounts and keep detailed logs of who has access.

Deploy AI-Aware Monitoring and Detection

Use security monitoring tools to record AI-related activity when employees use language models in email, collaboration tools, and productivity tools. Configure alerts for anomalous patterns, such as bulk data uploads to AI services or unusual prompt behaviour.

Integrate AI Risks Into Incident Response Procedures

Add AI-specific situations to your runbooks, such as prompt injection attacks, model compromise, and data theft through AI channels. Choose response team members with expertise in AI security to observe how models act during investigations.

Conduct Regular Third-Party Security Assessments

Check the security of your AI vendors, plugin providers, and model suppliers before you integrate them and at regular intervals after that. Maintain a list of approved vendors that details the security controls and data protection responsibilities that each vendor has under contract.

Align AI Controls With Compliance Frameworks

Identify compliance requirements such as GDPR, HIPAA, or SOC2 and map your AI security measures to those requirements to ensure audit readiness. Document how your AI system handles regulated data types and develop controls to meet industry-specific requirements.

Provide Ongoing Security Training for AI Users

Provide role-specific security training for employees to demonstrate how to protect sensitive data when using AI tools. Include real-world examples and testing to verify employees understand the policies and procedures for safe AI usage.

The regulatory environment governing how AI interacts with confidential data is expanding. Below are the “regulatory compliance frameworks that seek to address how LLMs can and cannot interact with sensitive data,” as paraphrased by Koduru in a related post on AI and Data Protection: Strategies for LLM Compliance and Risk Mitigation:

• GDPR has direct impacts on AI operations through data minimisation, consent, and erasure rights.
• CCPA requires that companies grant consumers rights over their data, such as access and deletion rights, which AI must respect when it processes large amounts of data.
• HIPAA’s stringent privacy and security rules for protected health information may complicate the use of AI in healthcare.
• FERPA, like HIPAA, establishes similar rules but for the protection of student education records and requires AI to be confidentially managed in educational settings.
• PIPEDA in Canada also focuses on accountability and consent for commercial organisations processing personal information.
• COPPA applies to commercial organisations collecting personal information from children under 13 and can affect AI applications involving minors.
• NIST Cybersecurity Framework (CSF) offers guidance for organisations to manage cyber risk in AI systems that collect or process sensitive data.

The EU’s AI Act will require new provisions for high-risk AI systems beginning August 2026. Additional provisions of the act include logging and traceability of AI system interactions. Other regulations that may apply to AI development and deployment include the EU’s ePrivacy Directive, China’s PIPL, Japan’s APPI, and Brazil’s LGPD.

Establish a baseline risk assessment for your organisation that maps where AI systems touch sensitive data across all departments of your business. Determine which teams use AI tools, what kind of data flows into those models, and where outputs get stored or shared. That visibility creates the foundation for every future control decision.

Governance models work best when they focus on enablement, not blockade. When security teams only focus on strict controls, they make things harder for users, which prompts them to rely on shadow AI. Instead, establish controls that put the user first so that AI can be safely used while still being watched. Deploy telemetry before implementing controls to establish baseline user behaviour patterns.

Your AI data security shouldn’t operate in isolation from SecOps and compliance workflows. Integrate AI logging with your existing SIEM infrastructure. Route AI-related incidents through established response playbooks. Coordinate with compliance teams so your AI governance complies with audit requirements without creating duplicate processes.

Programme metrics should keep track of both risk reduction and time to adoption. Keep an eye on how often prompts are filtered, how often data is exposed, how often shadow AI is found, and how satisfied users are with their permitted tools. The goal of governed enablement is to have tools and policies work together to boost productivity without risking security. Users choose approved AI solutions because they meet their business needs, not just because other options are blocked.

Proofpoint helps enterprises address AI data security through insider threat detection that identifies shadow AI adoption and misuse; DLP and classification controls that extend into AI interactions; and visibility across collaboration platforms where AI tools often operate. Compliance and audit features provide the logging and documentation frameworks that regulators expect. These capabilities enable governed AI adoption rather than forcing organisations to choose between innovation and security. Contact Proofpoint to learn more.