AI Runtime Security: How to Protect Your AI from New Threats

As AI becomes deeply embedded in business operations, the risks it faces at runtime are no longer hypothetical. Attacks such as prompt injection, model tampering, data exfiltration, and adversarial manipulation now target AI systems while they're in use.

AI runtime security is the responsibility of AI tool publishers—the vendors who build, deploy, and maintain the AI systems you use. As a consumer of these AI tools, you can't control their runtime environments, modify their security implementations, or access their internal monitoring systems. But you should understand what AI runtime security entails, why it matters, and what questions to ask vendors about their security practices.

More importantly, while you can't secure the runtime environments of third-party AI tools, you can, and must, monitor how these tools operate within your SaaS ecosystem: what data they access, which systems they integrate with, what permissions they hold, and whether their behavior introduces risk. Understanding AI runtime security helps you evaluate vendors and make informed decisions, while monitoring third-party AI tools in your environment gives you the visibility and control you need to protect your data.

What is an AI runtime environment?

An AI runtime environment is a specialized system for running and managing AI applications, similar to traditional software runtimes.

An AI runtime environment is also the execution layer where AI instructions are carried out, ranging from machine learning tasks to deep learning model inference. It governs how computational resources like CPU, GPU, and memory are allocated, optimized, and sustained during operation.

By abstracting away the complexities of low-level system orchestration, such as memory allocation, process scheduling, and hardware utilization, this environment enables developers to prioritize model development and training. It ensures stability, efficiency, and performance at scale.

At its core, the AI runtime environment serves as the critical link between high-level models designed by developers and the underlying operations required for execution. This orchestration is essential for the reliable and effective deployment of AI applications.

What is AI runtime security?

AI runtime security encompasses the protection of models and inference systems during active execution. It involves defending against adversarial inputs, unauthorized access, data leakage, and prompt injection attacks. The objective: preserve model integrity, safeguard data in motion, and ensure trusted, tamper-resistant outputs throughout the lifecycle of deployed AI systems.

According to Gartner®, "Runtime controls are also evolving. They preclude unauthorized sharing of sensitive data and enforce security and acceptable use policy for an organization. These solutions can be implemented in front of SaaS services for runtime observations and alerts on suspicious or unauthorized client-side inbound and outbound activity. They can also be implemented in-line to embedded AI SaaS or software when APIs exist therein, for example, as is the case with popular AI agent platforms. Runtime solutions are covered in our Market Guide for AI Trust, Risk and Security Management."¹

These runtime controls form a critical layer of defense as AI systems become more deeply embedded in business operations, handling increasingly sensitive data and making autonomous decisions that impact organizational outcomes.

Safeguarding data

AI systems routinely handle sensitive and mission-critical information. Robust runtime security measures are necessary to prevent data breaches and block unauthorized access, ensuring that data remains confidential and protected throughout processing.

Business continuity

AI systems are increasingly embedded in mission-critical workflows, from fraud detection and supply chain forecasting to autonomous decision engines. An attack or disruption at the runtime level can halt operations, corrupt outputs, or trigger cascading failures across dependent systems.

Robust runtime security acts as a safeguard against such threats, ensuring AI workloads remain stable, available, and uninterrupted. By securing the execution layer, organizations can maintain consistent service delivery, protect uptime service level agreements (SLAs), and reduce the operational and reputational risks associated with AI-driven outages.

Protecting AI investment

Protecting the significant capital invested in AI systems is a strategic imperative. These platforms often represent years of research, specialized talent, and substantial financial outlay. Robust security safeguards are essential, not only to defend against unauthorized access and data breaches, but to ensure the long-term viability and reliability of the AI infrastructure itself.

Secure AI environments protect against disruption, IP theft, and reputational damage. Comprehensive security across data, models, and communication channels maintains trust and maximizes AI ROI, ensuring resilience against evolving threats and regulations.

Maintaining user trust

Users entrust their data to AI-driven platforms with the expectation that systems are secure by design and protected in operation. Runtime breaches, whether through data leakage, unauthorized inference, or adversarial manipulation, erode this foundational trust.

Once compromised, reputational damage can extend beyond immediate users, affecting regulatory standing, partner relationships, and long-term customer retention. Implementing rigorous AI runtime security not only mitigates the risk of breach but also reinforces a visible commitment to responsible data stewardship.

In high-stakes sectors like finance, healthcare, and infrastructure, maintaining user trust is a core operational requirement.

Regulatory compliance

Regulatory compliance is a central concern for organizations deploying AI systems, particularly in sectors governed by stringent data security and privacy requirements. Industries such as finance, healthcare, and critical infrastructure are subject to frameworks like GDPR, HIPAA, and the EU AI Act, which mandate robust controls over data handling, model transparency, and access management.

AI runtime security enhances SaaS compliance by providing continuous monitoring, threat detection, and access controls throughout the AI application's lifecycle. This prevents data leaks and unauthorized access, thereby avoiding penalties and reputational harm. Solutions can block sensitive data in model outputs, enforce zero-trust security, and generate audit trails for reporting purposes.

Real-time observability and automated compliance mapping enable organizations to identify and mitigate AI risks, ensuring their systems align with legal and ethical obligations. Consequently, AI runtime security is essential for maintaining compliance and trust in AI operations.

What are the new risks, vulnerabilities, and threats to AI runtime security?

Attackers are developing sophisticated techniques designed to exploit AI models during execution. Understanding these threats is essential for evaluating vendor security practices and identifying the runtime protections that matter most.

Prompt Injection / Jailbreak Attacks

Adversaries exploit AI interfaces by injecting crafted inputs that override system instructions, effectively turning the model into a “confused deputy.” This allows attackers to escalate privileges during execution, forcing the model to bypass safety guardrails, generate prohibited content, or ignore hard‑coded restrictions set by developers.

Data Leakage

AI systems working on sensitive datasets remain vulnerable to inference and exfiltration attacks during execution. Continuous monitoring of runtime behaviors is essential to detect anomalous access patterns and prevent unauthorized data exposure.

Model Tampering

Runtime manipulation of deployed models — through memory injection, parameter alteration, or adversarial interference — can distort predictions or drastically alter behavior. Protecting model integrity in live environments is critical for maintaining trust and operational fidelity.

Denial of Service (DoS) Attacks

As with any digital system, AI runtime environments can suffer denial‑of‑service attacks that degrade performance or availability. Well‑planned resource controls and failover mechanisms help sustain uptime under stress.

Inference Attacks

Attackers query models to infer confidential information about training data or internal logic. Guardrails and output filtering reduce the risk of unintentional disclosure.

Adversarial Attacks

Malicious actors craft subtle, deceptive inputs that cause misclassification or flawed decisions at inference time, often bypassing validation layers.

RAG / Context Poisoning

Attackers inject malicious commands into documents, emails, or databases that the AI retrieves dynamically through Retrieval‑Augmented Generation. The runtime risk involves indirect prompt injection, where the AI ingests this trusted data and executes hidden instructions — such as exfiltrating private user data or generating corrupted answers — without the user realizing the context was compromised.

Reverse Engineering API Calls

Through systematic probing, adversaries reconstruct how AI systems interact with backend services, uncovering logic or data structures that can be exploited.

Supply Chain Attacks

Threat actors compromise third‑party libraries, pre‑trained models, or integrated components, introducing vulnerabilities that propagate through the AI system and erode trust.

How do you secure your AI runtime environment?

Securing an AI runtime environment requires controls that operate at the same speed and context depth as the models themselves. Traditional input validation and access management are not enough. The following practices focus on runtime‑specific defenses that prevent manipulation, data leakage, and misuse during active execution.

1. Semantic Input Filtering

Go beyond basic sanitization. Implement a pre‑flight security layer — a lightweight classifier or secondary LLM — to analyze incoming prompts for malicious intent, jailbreak patterns, and adversarial noise before they ever reach the core model. This early checkpoint prevents attacks like prompt injection and privilege escalation in real time.

2. Output Redaction and Integrity Checks

Embed real‑time scanning on model responses to automatically detect and redact personally identifiable information (PII) and other sensitive content before it’s returned to the user. Add integrity verification to flag hallucinated code, embedded commands, or unsafe URLs that could propagate downstream risk.

3. Deterministic Guardrails

Deploy rigid, programmable policy layers between users and the model. These guardrails enforce non‑negotiable rules such as blocking restricted topics, banning operational commands, and enforcing approved output formats. Deterministic logic ensures compliance even when the model’s probabilistic responses attempt to deviate.

4. Granular Tool and API Permissions

Apply the Principle of Least Privilege to the model itself. When the AI has access to external tools or APIs (e.g., plugins, retrieval systems, or database connectors), restrict it to read‑only permissions whenever possible. Require human‑in‑the‑loop approval for high‑impact actions such as database writes, file creation, or external messaging.

5. Rate Limiting and Resource Quotas

Prevent denial‑of‑service and “sponge” attacks by enforcing strict limits on API calls, token usage, and GPU compute time per user session. These quotas protect system availability and prevent attackers from exhausting expensive inference resources.

6. Real‑Time Observability and Audit

Instrument deep logging to capture not only inputs and outputs, but also intermediate reasoning traces (chain‑of‑thought summaries, tool calls, and context shifts). Monitor for behavioral drift and anomalous activity that may indicate data extraction or unauthorized task execution. Continuous observability enables rapid detection of runtime compromise.

7. Sandboxed Execution

Run AI‑generated code and high‑risk reasoning tasks inside isolated, ephemeral sandboxes with no network connectivity to internal systems. This containment prevents the model from being used as a pivot point for lateral movement or data exfiltration inside production environments.

What questions should you ask AI vendors about runtime security?

Evaluating AI vendors requires more than reviewing compliance checklists. You need to understand how their models behave during execution to know how they defend against manipulation, protect sensitive data, and maintain trustworthy outputs at runtime. The following questions focus on the controls that matter most for securing AI systems in operation.

1. Adversarial robustness

How do you test your models against semantic attacks such as jailbreaking, prompt injection, and forced hallucinations? Do you employ dedicated red‑teaming programs that probe for behavioral vulnerabilities beyond standard penetration testing?

2. Data retention and training

Do you guarantee stateless inference, ensuring that runtime data is never stored, logged, or reused for future training? If “quality assurance” data is retained, what safeguards prevent it from being memorized or surfaced to other tenants?

3. Context and tenant isolation

In retrieval‑augmented generation (RAG) architectures, how do you mathematically guarantee that one tenant’s queries cannot retrieve or influence embeddings and vector data belonging to another tenant? What monitoring exists to detect cross‑tenant data bleed or context contamination?

4. Agentic boundaries

If the AI can access external tools or APIs, how are those permissions scoped? Do you enforce human‑in‑the‑loop approval for high‑impact actions, such as database writes, file creation, or outbound communications, to prevent “confused‑deputy” behavior?

5. Output integrity

What mechanisms detect and block hallucinated code, embedded commands, or malicious URLs in the model’s responses? Do you perform real‑time PII filtering or output redaction to prevent sensitive information from leaving the runtime environment?

6. Model observability

Beyond uptime metrics, do you provide telemetry into token drift or semantic spikes that could signal a denial‑of‑wallet attack, data extraction attempt, or model degradation? Are runtime logs accessible for independent review or correlation with security monitoring tools?

7. Supply chain provenance

Can you supply a software bill of materials (SBOM) for the AI model, detailing the datasets, pre‑trained weights, and third‑party components used, to verify that no poisoned or compromised elements exist upstream?

Extending AI security with Vorlon's ecosystem visibility

AI runtime security is a critical discipline that protects models during execution, defending against prompt injection, model tampering, adversarial attacks, and a growing array of sophisticated threats. The systematic approach outlined here represents the foundation of responsible AI deployment.

For AI tool publishers, implementing these practices is non-negotiable. For consumers of AI tools, understanding these principles is equally important. When evaluating vendors, ask detailed questions about their security implementations, compliance certifications, monitoring capabilities, and incident response processes. The answers will reveal whether a vendor takes runtime security seriously or treats it as an afterthought.

But your responsibility doesn't end with vendor selection. Third-party AI tools operate within your SaaS ecosystem, accessing your data, integrating with your systems, and potentially introducing risk you can't see from the vendor's security documentation alone. You need visibility into how these tools actually behave in your environment: what data they touch, which systems they connect to, what permissions they exercise, and whether their activity aligns with your security policies.

Vorlon complements AI runtime security implementations by providing continuous monitoring and mapping of third-party AI tools in your SaaS ecosystem. While vendors secure their runtime environments, Vorlon helps you understand how those tools behave within your environment, detect anomalies, map data flows, and ensure that third-party AI integrations don't introduce unexpected risk. This ecosystem-level visibility extends your security posture beyond what you build to encompass what you use.

The convergence of AI and SaaS has created powerful new capabilities and powerful new risks. Securing AI at runtime is the vendor's job. Monitoring AI behavior in your ecosystem is yours. Together, these layers create the comprehensive defense needed to innovate confidently in an AI-driven world.

¹Gartner, Exposing and Managing Embedded AI: Tools for Transparency and Vendor Oversight, Avivah Litan, 21 July 2025. Available at: https://www.gartner.com/en/documents/6751234. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.