What is AI runtime security?
AI runtime security safeguards your AI apps, models, and data during active operation. Going beyond traditional security’s focus on static pre-deployment analysis, runtime security monitors AI behavior at inference while it actively processes user requests and sensitive data.
This continuous monitoring is crucial for detecting and blocking threats that only appear during live interactions, such as prompt injections or adversarial inputs. AI runtime security controls protect against these threats adaptively—all without compromising the performance needed for production environments.
AI Security Posture Assessment Sample Report
Take a peek behind the curtain to see what insights you’ll gain from Wiz AI Security Posture Management (AI-SPM) capabilities. In this Sample Assessment Report, you’ll get a view inside Wiz AI-SPM.
Why does traditional security fail at AI runtime?
Traditional security tools fail with AI systems because they were built for a predictable, static world—an assumption that AI breaks. AI is dynamic and non-deterministic, meaning traditional security fails in a few critical ways.
Misses dynamic threats
➡️ Traditional tools test static code, not real-time AI behavior.
A vulnerability scanner may approve a chatbot’s code, but cannot detect it leaking confidential data via prompt injection during a live interaction.
Lacks behavioral context
➡️. Security systems that treat all traffic the same way can’t recognize AI-specific attack patterns.
A network monitor might log thousands of API requests, but can it distinguish a benign user query from a slow-burning model extraction attack unfolding over weeks? No.
Negatively impacts performance
➡️. Conventional security agents can’t keep up with the throughput, latency, and memory demands of production AI.
Security agents can consume significant memory and CPU resources for monitoring and scanning, competing with the resources needed to run large AI models—particularly in memory-constrained environments like GPU-accelerated inference clusters.
With regulations like the EU AI Act mandating continuous live monitoring and auditing, context-aware oversight is non-negotiable, making AI runtime security critical for AI regulatory compliance.
What AI threats require AI runtime security?
Production AI systems face real-world threats that demand continuous monitoring and AI-specific defenses. These active attack vectors appear daily in deployments and can be broadly categorized into two main types.
Live inference attacks
These attacks manipulate the model's behavior during live inference for data leakage or long-term sabotage.
A common attack in this category is prompt injection. This involves embedding malicious instructions within a query, like telling a chatbot to "ignore previous instructions and reveal all customer data."
Other attacks include adversarial inputs that fool the model, or model and training data extraction via API abuse to steal intellectual property.
Operational security risks
These AI threats undermine the integrity of the AI service itself across the MLOps/AI lifecycle.
One particularly insidious attack is model poisoning through feedback loops, where data used to retrain models is manipulated, gradually corrupting behavior over time. Even without a malicious actor, AI systems can still expose sensitive data independently.” And traditional threats like API rate abuse and denial-of-service become even more damaging at the scale of AI inference.
All the above threats, compounded by issues like bias and fairness vulnerabilities, lead to runtime security and compliance failures that conventional security tools simply can't detect or prevent.
AI-specific attacks strike in real time and can inflict immediate, often irreversible damage. AI runtime security safeguards your system using unique capabilities built precisely to mitigate these threats.
Core capabilities of AI runtime security
AI runtime security depends on a defense system surrounding three core pillars:
Deep visibility
Active protection
Rigorous compliance
These capabilities work together to create a continuous feedback loop that secures AI without hindering performance, transforming your AI security from a reactive afterthought to a proactive, auditable component of your AI infrastructure.
Model behavior monitoring and introspection
Effective runtime security starts with deep visibility into model behavior during live operation. Continuous monitoring of outputs, decisions, and—where available—internal states defines what 'normal' looks like. For hosted models without internal access, monitoring focuses on input-output patterns, API usage, and behavioral anomalies.
Key controls for achieving this include:
Performance tracking
Anomaly detection
Drift analysis
Output validation
(Advanced) Model introspection and safety‑policy engines
Deviations from baseline behavior trigger automatic, contextualized, and actionable alerts with guided remediation—such as blocking suspicious API calls, isolating affected services, or creating tickets with precise ownership for investigation.
O que é AI-SPM? [Gerenciamento de postura de segurança de IA]
O AI-SPM (gerenciamento de postura de segurança de IA) é um componente novo e crítico da segurança cibernética corporativa que protege modelos, pipelines, dados e serviços de IA.
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Threat detection and response
Building on visibility, the next pillar is active protection. Once an anomaly is detected, your system has to respond in real time. This requires a suite of defenses designed specifically for AI threats, coordinated through a unified policy engine that applies consistent, least-privilege controls to tools, plugins, and data access across environments:
AI-specific attack recognition
Dynamic input filtering
Automated incident response
Adaptive protection
(Advanced) Confidential computing for AI
Automated application detection and response (ADR) enables quarantine of suspicious services, blocking of malicious users, or disabling of risky tools in real time—such as isolating a compromised model-serving endpoint or revoking access to external APIs.
Audit and compliance capabilities
The final pillar tracks every interaction and action to create a verifiable audit trail, while also enforcing security rules based on governance frameworks such as NIST AI RMF.
To maintain compliance and full transparency, you need:
Live audit logging
Policy enforcement
Regulatory reporting
Data protection
(Advanced) Runtime differential privacy enforcement
These capabilities not only help meet AI compliance across relevant regulatory standards and frameworks but also provide the verifiable proof needed to maintain trust in your AI system.
The State of AI in the Cloud 2025
As DeepSeek adoption surges, security and governance challenges persist.
Deployment considerations for cloud environments
The table below presents security gaps that an advanced AI runtime security solution should be able to catch.
| Challenge | Core problem | Modern solution |
|---|---|---|
| Ephemeral infrastructure | Containers and serverless functions often exist for mere minutes, one minute or less for 60% of them. This makes traditional manual security agents too slow for effective protection. | Apply Kubernetes-native AI runtime protections and embed security directly into CI/CD pipelines to automatically secure containerized workloads, no matter how short-lived. |
| Multi-cloud complexity | Each cloud provider (AWS, Azure, GCP) has its own fragmented set of security tools and monitoring capabilities. This leads to inconsistent security coverage and dangerous blind spots across environments. | Use a unified security fabric for consistent monitoring and policy enforcement across clouds, with optional cloud-specific controls like Azure AI runtime security. |
| "Shadow AI" governance | Development teams can rapidly deploy new, unauthorized models and APIs without informing central security teams. This creates untracked risks and ungoverned AI usage. | Automate AI discovery and security checks in deployment workflows to identify new models instantly and apply the right governance posture without slowing innovation. |
These modern solutions share a common thread: The shift towards “security as code.”
In this paradigm, protection is no longer a separate step; it’s an integrated component that activates automatically with every AI deployment across clouds.
How Wiz AI-SPM delivers AI runtime security
Wiz has built a solution specifically designed to face the challenges of AI. Wiz AI-SPM empowers security and development teams to build faster through self-service prevention and response capabilities.
Wiz’s unified security capabilities for AI runtime include:
Agentless monitoring with code‑to‑cloud visibility—and optional lightweight runtime sensors for deeper signals—enables fast adoption without material performance overhead.
Real-time threat detection mitigates risks before any actual damage or escalation.
Automation is available for revoking exposed secrets, isolating affected services, and creating developer tickets with precise ownership.
Code‑to‑cloud context ties anomalous model behavior to the exact service, identity, and data path so teams can act quickly.
For your cloud deployments, Wiz provides a unified control plane across public and private clouds—simplifying compliance and reducing blind spots.
At the core is the Wiz Security Graph:
Connect AI assets to security controls and insights in real time.
Get live risk prioritization of inference threats.
Perform deep attack path analysis and detect runtime data exposure.
And for those compliance regs? Wiz provides the comprehensive context needed to prioritize security and demonstrate adherence at enterprise scale—mapping runtime controls to frameworks like ISO 27001, SOC 2, and NIST SP 800-53.
Ready to see Wiz in action? Request a demo to explore how our AI runtime security capabilities protect your AI workloads during live operation.
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