AI Runtime Security

TL;DR

AI Runtime Security protects machine learning models and data pipelines during execution, defending against threats like prompt injection, adversarial inputs, and data leakage. By ensuring models behave safely and reliably, it helps maintain trust, compliance, and business-critical outcomes.

What Is AI Runtime Security?

AI runtime security is the discipline of protecting artificial intelligence and machine learning systems while they are actively running in production. Unlike traditional application security, which focuses on code, infrastructure, or network layers, AI runtime security protects the unique components of AI systems. This includes the models, training data, inference pipelines, and any sensitive information they process. Its goal is to prevent attacks that target these AI-specific elements.

What Problem Does AI Runtime Security Solve?

The rise of AI-native applications has created security challenges that conventional tools cannot address. Traditional runtime security solutions excel at monitoring system calls, network activity, and process behavior, but they lack visibility into how AI models learn, respond, and evolve.

This blind spot leaves organizations exposed to threats such as:

• Unpredictable outputs that appear anomalous but are part of normal AI behavior
• Prompt injection and adversarial inputs that trick models into unsafe or incorrect responses
• Model theft through systematic probing and query-based extraction
• Training data exposure when sensitive information leaks through outputs

AI runtime security solves these problems by providing the visibility and control needed to secure AI applications and the model layer itself, ensuring that AI logic cannot be misused or manipulated.

Why Does AI Runtime Security Matter?

AI is increasingly embedded into business-critical systems, from fraud detection to healthcare diagnostics to personalized customer experiences. These models drive automated decisions that directly affect trust, compliance, and revenue.

If compromised, AI systems can:

• Undermine customer confidence by producing biased, unsafe, or manipulated outputs
• Expose organizations to regulatory penalties through unauthorized data disclosure
• Cascade errors through automated workflows, amplifying the impact of a single malicious input
• Damage intellectual property value if proprietary models are stolen or cloned

In short, AI runtime security matters because it protects not just the models themselves, but the business outcomes, reputational trust, and compliance posture that depend on them.

What Are the Challenges of Securing AI at Runtime?

Securing AI at runtime is fundamentally different from securing traditional applications because AI changes the very nature of application logic. The qualities that make AI powerful, including adaptability, non-determinism, and speed, also make it more complex to secure. AI runtime security must combine granularity, context awareness, and continuous monitoring to reduce false positives while still detecting subtle but genuine threats.

Unpredictability by Design

Traditional applications are deterministic: given an input, the code produces a predictable output. AI-native applications, however, introduce components that generate outputs probabilistically. This makes the application itself unpredictable by design. What looks like an anomaly to a conventional security tool may actually be valid AI-driven behavior, and the reverse can also be true.

Anomalies by Design

AI outputs often break expected patterns. From the perspective of a firewall, SIEM, or other conventional tool, many legitimate AI interactions may appear irregular. Without deep context into how the application and its AI components actually work, these tools risk either flagging too many false positives or missing true attacks hidden in the noise.

Continuous Change

AI does not just run inside applications, it helps create them. Code generators and AI-assisted development tools can rapidly change application logic, introducing new versions and features at unprecedented speed. This accelerates innovation but also expands the attack surface, making it harder for security teams to keep pace.

Context Requirement

To separate noise from real threats, security tools need to see the entire execution flow: how a request triggers an endpoint, which code runs, which system calls are made, and how the AI component responds. Only with this end-to-end visibility can security differentiate between acceptable anomalies and malicious activity.

Adversarial Advantage

Attackers are also leveraging AI. They can craft adversarial inputs, automate prompt injection attempts, and probe models for data leakage, all of which blend easily into the unpredictable behavior of AI applications.

Lack of Standards

Because the field of AI security is still emerging, there are limited frameworks or benchmarks to guide best practices. Organizations are often left building custom approaches, which can increase complexity and cost.

How Does AI Runtime Security Differ From Traditional Runtime Security?

Aspect: Primary Focus

Runtime Security: Protects applications, workloads, and infrastructure during execution

AI Runtime Security: Protects AI/ML models and data pipelines during execution

Aspect: Key Threats

Runtime Security: Exploits, malware, insider threats, misconfigurations

AI Runtime Security: Prompt injection, model theft, adversarial inputs, data leakage

Aspect: Visibility

Runtime Security: Observes system calls, process behavior, and network flows

AI Runtime Security: Observes model queries, responses, training data access, and inference patterns

What Threats Does AI Runtime Security Protect Against?

AI runtime security focuses on mitigating threats unique to machine learning systems, including:

• Prompt Injection: Attackers craft inputs that cause models to override safeguards and produce unintended outputs
• Adversarial Inputs: Small, carefully designed changes to inputs that trick models into making incorrect predictions
• Model Theft: Extraction of model parameters or functionality through repeated queries, enabling intellectual property loss
• Data Leakage: Exposure of sensitive training data through model outputs or probing
• Model Poisoning: Manipulation of training data to corrupt model behavior
• Abuse of Generative Models: Using AI systems to create disinformation, deepfakes, or malicious code
  

By detecting and blocking these attacks in real time, AI runtime security helps ensure AI models remain trustworthy and resilient.

How Does AI Runtime Security Work?

AI runtime security employs a combination of monitoring, detection, and enforcement techniques tailored to AI environments:

• Runtime Monitoring: Observes queries, responses, and data flows to detect suspicious or anomalous activity
• Behavioral Baselining: Learns what normal model interactions look like to flag deviations
• Input/Output Validation: Screens prompts and responses to block injection attempts, bias exploitation, or unsafe content
• Policy Enforcement: Applies guardrails that restrict how models can be queried and what they can return
• Anomaly Detection: Identifies unusual usage patterns that may signal probing, data exfiltration, or theft

These capabilities are increasingly embedded into MLOps pipelines, API gateways, and runtime security platforms to provide layered defense.

How Does AI Runtime Security Fit Into Existing Security Stacks?

AI runtime security does not replace traditional security; it complements it. While WAF, EDR, and SIEM protect infrastructure, endpoints, and networks, AI runtime security safeguards the intelligence layer that increasingly drives business logic.

A key aspect is enhancing the tools organizations already rely on. For example, Web Application Firewalls (WAFs) are effective at filtering known threats at the edge, but they were not designed to detect prompt injection or adversarial AI inputs. AI runtime security adds context by monitoring execution flows and model interactions, helping existing defenses separate noise from genuine risk.

It integrates with:

• XDR (Extended Detection and Response) to correlate model-level anomalies with broader attack campaigns
• SOAR (Security Orchestration, Automation, and Response) to enable automated containment of AI-specific risks
• PEM (Preemptive Exposure Management) to prioritize exposures in AI pipelines before they are weaponized
• ADR (Application Detection & Response) to provide application-aware visibility into how AI-driven components behave in real time
  

This layered approach ensures that AI models are not an overlooked weak point in enterprise defenses and that existing security tools can adapt to the realities of AI-native applications.

What Does the Future of AI Runtime Security Look Like?

As AI adoption accelerates, runtime security will become a standard pillar of enterprise defenses. The future will likely include:

• Predictive Defenses: Applying machine learning to anticipate adversarial inputs and model manipulation before they occur.
• Deeper Integration: Embedding AI runtime protections directly into MLOps pipelines, CI/CD workflows, and cloud-native environments.
• Automated Remediation: Leveraging SOAR and AI-driven operations to contain and respond to threats in real time.
• Governance Alignment: Integrating with AI ethics, compliance, and risk management frameworks to ensure responsible use.
  

Some vendors are already pushing in this direction. For example, Miggo is extending runtime protection to AI-driven applications through capabilities like WAF Copilot, which augments traditional web application firewalls with AI-aware detection, and ADR, which gives security teams end-to-end visibility into execution flows. These approaches reflect how the market is evolving toward solutions that bridge the gap between legacy controls and AI-native risks.

Reach out to our team to learn how our solutions can provide the visibility and control you need to secure your data against hidden threats.

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