Securing Data in Use in the Age of AI

AI applications are deployed and embedded into our daily operations, from intelligent assistants to complex automated systems. AI isn’t magic, but rather a combination of math and data. Data is the fuel, engine, and navigation system that powers the AI application. But the same data that powers intelligence can also pose a threat if not protected, especially when data is in use during AI processing.

"The same data that powers intelligence can also pose a threat if not protected"

Data in Use: The Lifeline of AI Applications

Data is the fundamental engine powering AI systems, from shaping large language models (LLMs) outputs to driving agent actions and fueling intelligent decision-making.  This dynamic flow of information, as it moves through various stages of AI processing pipelines, is what we refer to as data in use for AI. This includes:

• Input data streams flowing into AI models for inference
• Training data is processed during model development.
• Feature data is extracted and transformed in real time.
• Model outputs and predictions are being generated and transmitted
• Feedback loops where AI decisions influence subsequent data flows

These AI systems are driven by vast and ever-changing data sources:

• User inputs (chats, instructions, documents)
• Enterprise knowledge bases (wikis, internal tools, API responses)
• Third-party data streams (integrated SaaS tools, cloud APIs)
• Behavioral context (session history, plans, agent memory)

Unlike traditional data processing, AI systems often handle this data in complex, multi-stage pipelines that can span multiple environments, from edge devices to cloud infrastructure. This data informs every decision, every action, and every sentence generated by an AI system. It’s not static, it’s fluid, contextual, and deeply intertwined with how AI behaves. This creates a unique attack surface that traditional security measures weren't designed to address.

Critical Security Concerns Around AI Data in Use

The fundamental role of data in powering AI systems creates unique and amplified security risks that organizations must address:

1. Exposure of Sensitive Data During Processing: Data faces exposure risks even when properly encrypted during transmission and storage. Once decrypted for AI model processing, sensitive information becomes vulnerable to memory-based attacks, compromised processing environments, and security flaws in the AI system itself. This processing phase represents a critical security gap where traditional encryption protections no longer apply.

2. Prompt Injection and Data Poisoning: A common AI-specific attack is prompt injection, where malicious inputs manipulate the AI to reveal sensitive training data, internal logic, or perform unintended actions during runtime processing. Beyond prompt injection, AI systems face data poisoning attacks that corrupt AI training processes to manipulate future decisions, and adversarial inputs crafted to cause AI systems to leak confidential data through their responses. All these vulnerabilities exploit the AI's processing mechanisms, creating security risks that emerge specifically from how these systems interpret and respond to inputs.

3. Data Leakage in Shared AI Pipelines: In shared AI pipelines, the high-volume, high-velocity nature of data processing amplifies the risk of unintended data exposure. Common issues include accidental data mixing during batch operations, where one tenant’s inputs or outputs bleed into another’s session, and privilege escalation attacks that exploit shared AI infrastructure to gain unauthorized access. There's also the risk of shared resource contamination, where insights or patterns learned from one source of data inadvertently influence the outputs served to another.

4. Agentic AI Risks: Autonomous AI agents, by their nature, can interact with diverse systems and execute actions. If compromised at runtime, an agent could exfiltrate data, perform unauthorized transactions, or even disrupt critical infrastructure.

5. Supply Chain Attacks: AI systems rely heavily on third-party models, libraries, and external data sources, which create complex dependency chains that introduce significant runtime risk. Vulnerabilities in these components can be exploited to trigger cascading security failures, especially as data flows dynamically through AI pipelines. The interconnected nature of these dependencies means that a security compromise in any component can propagate throughout the entire AI system, making traditional perimeter-based security approaches insufficient.

The Critical Need for Runtime Protection in AI

While encryption of stored and transit data is essential, it doesn’t address the unique risks introduced by AI systems. The critical vulnerability exists during runtime operations when AI applications actively process data, execute model inference, and facilitate agent interactions. This execution phase is where sensitive information is most exposed, and traditional security controls fall short. That’s why runtime protection is no longer optional—it’s essential.

Here’s how runtime security specifically addresses the core challenges of modern AI environments:

The Data-Centric Nature of AI Security: Traditional security measures were designed for applications where data processing was predictable and limited in scope. AI systems fundamentally change this paradigm: they are driven by data, shaped by dynamic context, and constantly evolving in real-time. As a result, AI introduces new types of vulnerabilities that static security methods can't detect, especially when data is actively being used. This is where runtime protection becomes essential: it brings security into the very heart of AI execution, offering real-time defense as data flows, models infer, and agents act.

Volume and Velocity: AI systems process massive datasets at unprecedented speeds, making traditional scanning and inspection methods inadequate. A single AI training job might process terabytes of data in hours, while inference systems handle thousands of requests per second. Instead of relying on traditional batch scanning methods that can't keep pace with terabytes processed in hours or thousands of inference requests per second, runtime protection deploys intelligent filtering, anomaly detection, and automated response mechanisms that operate at the speed of AI processing itself.

Dynamic Data Relationships: Runtime protection tackles the challenge of AI systems discovering hidden relationships and creating new correlations by implementing context-aware security policies that monitor data combinations in real time. Rather than just protecting individual data elements, runtime security analyzes the semantic relationships AI systems create, detecting when "non-sensitive" data combinations become highly sensitive through AI processing and automatically applying appropriate protection measures to prevent unauthorized insights or data leakage.

Continuous Evolution: Unlike static applications, AI systems continuously learn and evolve, changing how they process and respond to data. Security measures must adapt to these changes in real time. Runtime protection is designed to be as dynamic as the AI it protects. It can continuously monitor the AI model's behavior, identify deviations from normal operations, and adapt security policies based on the evolving model. This real-time adaptability allows security to keep pace with the AI's learning and inference cycles, providing continuous defense against new attack vectors that emerge from the AI's evolving capabilities.

How Operant’s AI Gatekeeper Secures Data in Use at Runtime

The 3D Runtime Defense capabilities built into Operant’s AI Gatekeeper represents a new class of security solutions designed specifically to protect AI Data in Use during runtime operations. It provides end-to-end runtime visibility, control, and protection for your AI systems.

Here's how it addresses the unique challenges of AI data security:

‍End-to-End Runtime AI Protection: AI Gatekeeper deploys as a seamless layer across your AI agents and models. It sees everything as it happens, no delays, no batch scans, intercepting inputs, outputs, tool calls, and memory access in real time without the latency or blind spots. This continuous monitoring architecture ensures that every interaction with your AI agents and models is secured at the moment of execution, creating an adaptive defense system that evolves with your AI workloads.

‍AI-Specific Threat Detection and Response: AI Gatekeeper delivers real-time detection and mitigation of AI-native threats such as prompt injections, jailbreaks, model extraction, and data exfiltration through output leakage. It provides comprehensive visibility into AI data flows and processing activities, using behavioral analysis to identify unusual or high-risk actions across models, agents, and tools. Gatekeeper integrates with existing SIEM systems for centralized monitoring and enables automated incident response, allowing security teams to respond instantly and decisively to evolving AI threats.

‍In-line auto-redaction for sensitive data: AI Gatekeeper's automated redaction engine continuously monitors AI system interactions to identify and sanitize sensitive information in real time. The system automatically detects and redacts credentials, API tokens, personally identifiable information (PII), financial data, and other confidential information before it can be processed, stored, or transmitted by AI models. By operating at runtime, this in-line redaction ensures that private data is never exposed during inference or execution, even in the presence of adversarial prompts or insecure downstream components. This protects organizations from unintended data leakage while enabling safe, compliant use of AI systems.

‍Tool & Agent Identity Verification: Operant AI Gatekeeper enforces strict identity and access controls for AI tools and agents, ensuring that only trusted non-human identities (NHIs) can operate within defined permission boundaries. Authenticating tools and agents using metadata-driven verification prevents tool spoofing, unauthorized cross-agent interactions, and unapproved data access. This identity verification and access control mechanism is critical to securing Data in Use, as it ensures that only authenticated and authorized AI components can access or process sensitive information during runtime.

‍Policy-Based Enforcement: AI Gatekeeper allows teams to define zero-trust policies for what data agents are allowed to access, which tools they can call, and how models can behave in context. With these policies, teams can monitor for anomalous data access patterns that could indicate compromise and actively enforce prevention in real time.

‍Real-time Catalogs and AI Security Graphs: AI Gatekeeper maintains real-time data flow catalogs of AI workloads, tools, and models, and provides in-depth analytics on blocked threats, offering continuous visibility into the security posture of deployed AI systems. The platform creates cohesive AI Security Graphs that map high-risk data flows between AI workloads, agents, and APIs across different platforms, providing deep visibility into potential attack vectors.

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Live Data, Live Threats, Live Defense with Runtime Protection

As AI systems become more sophisticated and handle increasingly sensitive data, the need for specialized runtime protection becomes critical, whether you are deploying AI in production through copilots, customer-facing agents, or internal workflow bots. The attack surface is evolving faster than traditional tools can adapt, and AI threats don't wait for patch cycles, making it impossible to ignore data in use without severe consequences.

Organizations that implement runtime protection today won’t just safeguard sensitive data, but they’ll create a secure foundation for innovation and future growth. Solutions like Operant’s AI Gatekeeper represent the evolution of cybersecurity, purpose-built for AI environments that can bring runtime security to the AI stack, ensuring that innovation and protection go hand-in-hand. The question isn't whether you need runtime AI data protection, but how quickly you can implement it to stay ahead of evolving threats and secure your AI's lifeblood where it truly lives.

We invite you to try Operant’s powerful Runtime AI Protection platform to see for yourself how easy comprehensive security can be for your entire AI application environment.

‍Sign up for a 7-day free trial to experience the power and simplicity of 3D Runtime Defense for yourself.