Thales | Security for What Matters Most
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Thales | Security for What Matters Most
More About This Author >
As artificial intelligence (AI) systems become increasingly integrated into critical applications—from healthcare and finance to autonomous vehicles and industrial automation—the security of these systems during their runtime has emerged as a paramount concern. AI Runtime Security refers to the set of practices, tools, and technologies dedicated to protecting AI models and applications while they are actively running and making decisions in real-world environments.
What is AI Runtime Security?
AI runtime security is the discipline and practice of safeguarding the entire AI ecosystem during its operational phase. Rather than focusing solely on the AI model, runtime security covers all components running in the operation of AI—such as AI applications, vector databases, retrieval-augmented generation (RAG) systems, model-centric platforms (MCP servers), and the underlying AI models—to ensure that enterprise AI systems remain resilient, trustworthy, and compliant as they run in production environments.
Why is AI Runtime Security Critical?
AI thrives on large datasets. Organizations deploying AI need to give it access to large amounts of enterprise data that may include sensitive or proprietary data. Obtaining this data is the main objective of attackers targeting enterprise AI ecosystems.
As enterprises increasingly integrate AI technologies into their business operations, the AI ecosystem has grown more complex—encompassing not only third party AI models but also applications, data storage solutions like vector databases, retrieval-augmented generation (RAG) systems, and management/control plane (MCP) servers. This interconnected environment introduces a broad attack surface that adversaries can exploit. Ensuring AI runtime security is critical for safeguarding sensitive data, maintaining trust in AI-driven decisions, preventing model manipulation, and ensuring operational continuity.
Without robust runtime security measures, enterprises risk data breaches, leakage of proprietary models and intellectual property, adversarial attacks compromising AI outputs, and disruptions in AI services that can lead to financial and reputational losses. Furthermore, regulatory compliance mandates strong security posture when managing customer, personal and other sensitive data. Therefore, securing the entire AI runtime environment—not just the AI models themselves—is essential to realize the full benefits of AI while mitigating escalating risks. A strong AI runtime security posture not only mitigates risks but also fosters confidence among users, regulators, and businesses, enabling the transformative potential of AI to be realized securely and responsibly.
The Key Challenges in AI Runtime Security
AI runtime security needs to effectively protect AI systems, models, and their outputs during operation — after deployment and while they are actively processing data. This introduces several unique and complex challenges compared to traditional IT security or even general AI development security.
- Complexity of the AI ecosystem
Modern AI deployments are multifaceted, involving interconnected components such as AI applications that integrate machine learning inference, vector databases storing semantic embeddings, RAG pipelines combining enterprise data with AI outputs, and MCP servers managing model deployment and scaling. Securing each component individually, as well as the interactions among them, creates a complex surface vulnerable to diverse attack vectors. - Real-time data processing with security implications
AI ecosystems continuously ingest, process, and generate information in real time. This creates challenges around detecting suspicious activities, anomalous input data, or unauthorized queries that could lead to model compromise or data leakage, especially when large datasets and external knowledge sources interact dynamically via RAG and vector DBs. - Data privacy and compliance across ecosystem
Sensitive personal or enterprise data flows through various AI components. Vector databases might store embeddings derived from regulated data, AI applications expose APIs, and MCP servers facilitate model inference. Ensuring that sensitive data security and privacy are maintained throughout the pipeline—while meeting regulatory requirements such as GDPR, NYDFS, HIPAA, or industry-specific standards—is essential to avoid legal and reputational risks. - Adversarial and injection attacks beyond the model
Threat actors increasingly exploit indirect paths, such as malicious inputs to RAG systems or carefully crafted queries targeting vector DBs, aiming to manipulate AI outputs or extract proprietary information. These attacks demonstrate the need to secure every layer, including data retrieval, intermediate storage, and application logic—not just the AI model itself. - Maintaining model and data integrity in distributed environments
AI ecosystems often span on-premises systems, cloud services, and edge devices. This distribution can introduce vulnerabilities in the storage, transmission, and processing of models and data. Ensuring integrity across these environments—such as verifying that vector DB contents have not been tampered with or that MCP servers enforce strong authentication—is critical. - Dynamic Threat Landscape and Need for Continuous Adaptation
AI technology and attack strategies evolve rapidly. New vulnerabilities may emerge in vector database engines, novel adversarial techniques may target retrieval systems, and compliance standards may be updated. Runtime security must be adaptive, incorporating continuous monitoring, threat intelligence sharing, and automated response capabilities.
Strategies for Enhancing AI Runtime Security
To address these multifaceted challenges across the AI ecosystem, enterprises should implement a comprehensive strategy covering technology, processes, and governance:
- Robust threat modeling and risk assessment
Develop threat models that consider attacks targeting each AI ecosystem component—applications, vector databases, RAG pipelines, MCP servers, and deployed models. Assess risks by mapping potential vulnerabilities, attack vectors, data sensitivity and the impact of compromise on confidentiality, integrity, availability, and compliance. - Identify and classify sensitive data
The most important step to prevent a data breach originating from the AI ecosystem is to identify and classify data before ingestion into AI. Use robust discovery and classification tools to identify personal, proprietary or regulated data across Hybrid IT before ingestion into AI and define which can be used and which should be protected or de-identified. - Protect sensitive data and comply with regulations
Enforce data protection policies on sensitive data based on regulatory guidelines, encrypting or tokenizing sensitive data before ingestion into AI systems as well as data stored in vector DBs, logs containing inference data, and backups. Leverage key management practices to protect data on-premises and in the cloud and use industry-standard cryptographic protocols to protect data moving between AI applications, vector databases, model servers, and user interfaces. - Robust monitoring and anomaly detection across systems
Implement continuous logging and monitoring that captures usage of all AI systems and their interactions. Use AI-powered security analytics to detect unusual patterns—such as atypical vector DB queries, rapid changes in retrieval results, or unexpected API usage—that may signal attacks or system faults. - Granular access controls
Enforce strict access policies using role-based access control (RBAC) or attribute-based access control (ABAC) across all layers. For example, limit who can query or modify vector databases, restrict API access in AI applications, and secure MCP server administration interfaces. Apply the principle of least privilege consistently and use tools that can enforce centralized policies across multiple systems. - Input validation and output monitoring
Secure the intake of data into AI pipeline before they reach vector DBs, RAG modules, or models—to prevent injection attacks, data poisoning, or adversarial perturbations. Monitor output to prevent leakage of sensitive data or internal instructions. - Model and Vector DB integrity verification
Regularly audit models deployed in MCP servers and embeddings in vector databases to detect unauthorized changes or corruption. Techniques can include digital signatures, checksums, or hardware-based trusted execution environments (TEEs). Maintain version control and provenance metadata to support investigations. - Secure configuration and patch management
Maintain updated software for all AI ecosystem components, including vector database engines, RAG orchestration layers, AI application frameworks, and MCP servers. Harden configurations according to best practices, disable unused services, and regularly audit system settings to prevent exploitation. - Incident response and recovery plans
Develop and routinely test incident response plans that cover AI runtime security incidents. Include procedures for isolating compromised components, restoring trusted states, notifying stakeholders, and complying with regulatory breach notification requirements. Ensure rapid recovery minimizes operational disruption.
A focus on AI runtime security gives organizations the ability to move beyond protecting isolated models or applications and instead encompass the entire AI ecosystem an enterprise uses to develop, deploy, and maintain AI solutions. By addressing vulnerabilities across AI applications, vector databases, retrieval-augmented generation systems, MCP servers, and underlying models, organizations can safeguard data privacy, ensure model integrity, meet compliance demands, and defend against advanced threats—thereby enabling secure, trustworthy, and scalable AI deployments that drive business value.