AI Runtime Security: How to Protect Your GenAI Stack from Real-World Threats

According to a January 2025 Gartner webinar poll, 64% of organizations plan to launch agentic AI initiatives within the next year—40% of them within just six months—signaling a rapid shift toward autonomous AI adoption at the enterprise level.

As organizations accelerate adoption of agentic AI, securing these autonomous systems at runtime becomes critical—especially as their real-time decision-making increases the risk of prompt injection, data leakage, and unpredictable behavior. As AI adoption and deployment move from pilot to production, the question is no longer if AI interactions should be secured—but how. This makes AI runtime security a foundational layer in any enterprise GenAI strategy.

What is AI Runtime Security—and Why Does It Matter Now?

AI Runtime Security is the discipline of protecting AI systems while they're live and operational. It's where inference happens—where users interact with every component in the AI ecosystem—and where the most unpredictable, and potentially dangerous, behaviors can occur. This includes safeguarding:

• Model inputs – such as prompts, uploaded files, or API calls from applications 
• Model outputs – including generated text, decisions, or actions triggered by the AI 
• App and plugin interactions – how applications, tools, or third-party services interface with models during runtime 

System-level signals and behavior – such as API responses, latency trends, tool invocation patterns, or anomalous execution flows

In traditional software, runtime security is well-established. But advanced AI models introduce novel challenges—models can be manipulated, outputs can leak sensitive data, and adversarial users can trigger unintended behaviors even in well-tuned systems.

What’s different now is scale and exposure. As AI systems power more core business functions and external interactions, the risk surface expands—making runtime visibility and control essential to safe, scalable deployment

Scaling AI Safely: A Priority for Modern Executives

Nearly half of organizations in a recent McKinsey’s AI survey reported real-world consequences due to AI-driven inaccuracies or security-related incidents. The report also highlights that organizations where AI adoption is driven by executive leadership are scaling faster and unlocking real financial impact—from operational efficiencies to new revenue streams.

Today, CEOs, CISOs and CIOs are stewards of both innovation and risk. As AI powered tools like chatbots, copilots, and autonomous agents become part of customer- and employee-facing platforms, the exposure grows:

• Prompt Injection Attacks: Users manipulate prompts to override intended behavior. 
• Data Leakage: Sensitive or proprietary information is exposed in model outputs. 
• Toxic or Biased Outputs: Harmful content can be generated in real time, damaging brand trust. 
• Adversarial Inputs: Specially crafted inputs can fool or destabilize the model.

Ignoring runtime risks doesn’t just endanger infrastructure—it endangers the business. AI Trust, Risk, and Security Management (TRiSM) principles highlight the growing need for runtime monitoring, risk mitigation, and secure governance across the AI lifecycle—capabilities that an enterprise-grade AI security platform should deliver by design.

What Strategic Actions are needed?

1. Establish AI Runtime Monitoring

Continuously monitor AI models in production to detect anomalies, latency issues, and potential security threats. Real-time oversight is essential for identifying and mitigating risk. Robust observability enables teams to track agent actions, model behavior, plugin/tool usage, and output patterns—along with alerts for prompt injection, model drift, unauthorized tool access, and exposure of sensitive or personally identifiable information (PII). A secure AI gateway enhances this observability by logging and analyzing interactions, empowering teams to detect, investigate, and manage AI behavior across both structured and agentic workflows—accelerating secure development.

2. Implement Guardrails

Enforce guardrails that apply policies to inputs, outputs, and tool use in real time—helping prevent unsafe behaviors in agentic systems. Acting as policy enforcement layers, they block unsafe completions, restrict tool calls, and ensure AI responses align with enterprise intent. In agentic workflows, where models take multi-step actions or invoke tools, guardrails provide critical oversight without limiting autonomy. Core capabilities include redaction, response filtering, tool-specific permissions, and behavioral triggers tied to policy violations. As a foundation of agentic security, these dynamic controls reduce exposure, ensure compliance, and support safe, scalable AI across structured and flexible use cases.

3. Strengthen Access Controls Across the Stack

Secure your GenAI environment by ensuring access to models, data, and APIs is intentional, auditable, and based on need—not assumption. Implement role-based permissions, authentication, and usage boundaries to minimize exposure. This kind of policy-driven access control helps reduce risk while supporting team agility.

4. Ask for Vendor Transparency

Collaborate with AI and LLM providers that offer clear visibility into model behaviors and support runtime guardrails. Transparency helps organizations understand model performance, detect drift, and ensure that third-party models align with internal policies. Combining vendor transparency with a secure AI gateway allows enterprises to enforce access controls and monitor third-party model usage.

5. Invest in Red Teaming

Proactively test AI systems using red teaming techniques and adversarial simulations to identify and address vulnerabilities before they cause harm. These simulations expose weaknesses in prompt handling, tool execution, logic, and access control. An AI security platform with telemetry, attack simulation, and runtime analysis capabilities enables teams to catch misbehavior and policy violations early.

6. Coordinated Security and ML Practices for Safer, Scalable AI

Encourage close collaboration between security and ML engineering teams to establish unified governance. Cross-functional workflows ensure model deployments are both innovative and secure. A centralized security gateway supports this by standardizing access control, usage auditing, and oversight.

7. Prepare for Regulation

As GenAI adoption grows, so do the risks. Industry standards like the OWASP Top 10 for LLMs offer practical guidance on defending against common threats such as prompt injection, data leakage, and insecure plugin access. Meanwhile, regulatory frameworks like the EU AI Act and NIST AI RMF are quickly shaping compliance expectations. Embedding runtime security, auditability, and privacy-preserving techniques—such as encryption, redaction, and policy enforcement—into your AI stack will help you stay ahead.

By adopting these strategic actions and leveraging comprehensive AI security platforms—centered around a secure AI gateway—organizations can manage the risks associated with GenAI deployments, enabling innovation while maintaining strong security and compliance postures.

Secure the GenAI Stack Before You Scale

GenAI is transforming how enterprises operate, compete, and deliver value—but its full potential comes with new layers of risk. Without a strong runtime security into the GenAI stack, even the most advanced models become liabilities.

This is a critical moment for technology leaders. The decisions made today - around governance, access, monitoring, and trust - will define whether GenAI becomes a force for growth—or a source of risk. Securing GenAI isn’t just about model behavior—it’s about controlling every layer of the stack: inputs, outputs, orchestration layers, plugin usage, and user access.

By embedding security into the foundation of your AI systems, you can accelerate adoption, stay ahead of regulation, and protect what matters most—your data, your users, and your reputation.

With a secured stack you can innovate safely, at scale.

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