Businesses are no longer just using AI chatbots to ask and answer questions. These days, it’s all about autonomous AI agents being unleashed inside corporate systems, reading through files and directories, making alterations to them, automating workflows through APIs and executing live code.
Agents are automating everything, and they’re evolving as fast as they work. That’s why a static snapshot taken during the development process is totally inadequate to defend these systems against cyberattacks. Truly, AI adoption in the enterprise has outrun the ability of most security teams to watch what these systems actually do once they are live.
Security teams are learning the hard way that traditional static scanning tools and pre-deployment configuration checks are not enough. In fact, the best way to protect an AI system is to monitor the environments that it lives in, as it operates. Pre-deployment checklists and one-time model scans cannot tell a security team whether an agent is behaving normally at 2am on a Tuesday. That gap, the space between a system passing review and a system running unsupervised in production, is what runtime security is built to close.
As per the 2026 CNCF Annual Cloud Native Survey, 66% of organizations already hosting generative AI models run some or all of their inference workloads on Kubernetes. Securing AI systems, and their various connections to other resources, is certainly different from other types of software security, but it helps to know that most production AI now sits inside infrastructure that runtime security has spent a decade learning to watch continuously.
In this article, we’ll take a look at six runtime security offerings that use very different methods to keep tabs on the live threats facing AI systems when they’re up and running in production.
Agentless tools are widely used for cloud security today, but they didn’t start out covering runtime at all. The earliest agentless tools had a narrow focus on posture management and configuration risk, connecting to cloud environments through read-only APIs to quickly map an organization’s footprint. That approach is genuinely blind to what happens during active execution, since a configuration snapshot cannot tell a team whether a vulnerable library is being exploited in memory right now.
That’s the gap that AI workloads have exposed. Short-lived containers and agents can only be secured by observing what they’re doing during active execution, and that requires runtime visibility that pure posture scanning was never built to provide.
Rather than staying agentless-only, vendors like Wiz extended their platforms with a lightweight eBPF-based sensor built specifically for that job, adding real, agent-based runtime enforcement on top of the existing agentless foundation rather than trying to stretch configuration scanning to cover something it was never designed to do.
As one of the pioneers of agentless cloud security, it’s no surprise that Wiz continues to innovate at the runtime layer with the Wiz Sensor. It’s built on eBPF, which enables kernel-level event monitoring on Linux hosts and Kubernetes nodes in real time without significant resource overhead.
A separate Windows-specific version of Sensor, using Windows kernel telemetry rather than eBPF, extends the same real-time monitoring to Windows workloads. Together, the sensors keep tabs on system processes, network connections, and file modifications running inside containers and virtual machines.
The idea is that the sensor shows what’s happening in real time, while Wiz’s agentless posture management shows what else could go wrong elsewhere in the environment. The one-two punch of agentless and eBPF lets security teams see what’s happening inside live AI models, app integrations and standalone agents, making it easier to actively block anomalous behavior, such as an LLM attempting to exfiltrate sensitive data without authorization.
Not deployed automatically across every workload, the runtime sensor is an add-on to Wiz, so it’s worth remembering that organizations that don’t specifically configure and roll it out to their production systems are still only getting agentless posture visibility, not real-time blocking.
Sysdig offers dedicated runtime detection rules for coding agents such as Claude Code, Gemini CLI, and Codex, built on Falco, the open source runtime security engine Sysdig created in 2016. The company has spent close to a decade monitoring cloud-native runtime environments with Falco, and it leverages that experience to extend coverage to AI-specific workloads.
With Sysdig’s tools, security teams can surface indicators of compromise amid an ocean of legitimate agent activity. For example, a coding agent that tries to access a sensitive credential store will trigger flags as a deviation from the norm.
That level of telemetry ultimately feeds into Sysdig’s broader AI Workload Security capabilities, which map live attack paths so teams can prioritize and respond to the alerts that matter most.
On the other hand, Sysdig’s AI-specific detections are less mature than the company’s decade-old Kubernetes runtime engine, so organizations running significant AI infrastructure on Windows or outside containerized environments may find the coverage thinner than what Sysdig offers for Linux-based workloads.
With Prisma AIRS, Palo Alto Networks provides threat protection across the AI development lifecycle. The company’s AI Runtime Security solution is the piece built for live production workloads, and it inspects application traffic in real time to block prompt injection, data leaks, malicious code executions, and resource abuse.
That runtime layer was built natively as part of Prisma AIRS; the company’s 2025 acquisition of Protect AI added a separate capability, model and supply chain scanning, rather than the runtime blocking itself. With Prisma AIRS 3.0, that runtime coverage extended further into agent-specific behavior, including tool misuse and memory manipulation as autonomous agents act in production.
The company is positioning Prisma AIRS as a single, integrated platform spanning runtime protection, supply chain scanning, model security, automated red teaming, and agent identity, aimed at organizations that want to avoid stitching together fragmented point tools.
Because AIRS was assembled from several acquisitions, including Protect AI, buyers should expect some ongoing integration work as those underlying detection engines continue to consolidate into a single product.
HiddenLayer started as a research effort focused on model tampering, manipulation, and intellectual property theft, before growing into a platform built to defend against those same threats. That lineage shows up in its runtime product, which watches live model behavior for signs of drift or adversarial inputs rather than focusing primarily on the prompt layer.
The company built this on the same foundation as its supply chain scanner, which inspects model files for hidden or malicious code much like antivirus software inspects executables.
By carrying that scrutiny into runtime, the platform gains useful context on what baseline “normal” behavior looks like for a given model, giving it signals to validate models both before and while they’re running in production.
HiddenLayer’s focus on model-level defense means prompt-layer guardrails for chat-style applications aren’t the platform’s core strength, so teams building conversational AI features often pair it with a dedicated runtime guard rather than relying on it alone.
Lakera Guard draws on data from Gandalf, the public game where players try to talk an AI model into revealing a hidden password. Gandalf has attracted more than a million players worldwide, and the resulting trove of adversarial prompts feeds directly into the detection models behind Lakera Guard.
Acquired by Check Point in 2025, Lakera Guard sits between the user and the model, inspecting every prompt and response to catch jailbreak attempts, data leakage, and attacks embedded in retrieved documents or files.
It’s built for sub-50 millisecond latency to minimize the hit on model performance, and it works with any LLM, regardless of provider, which makes it a natural fit for monitoring customer-facing chatbots and AI assistants.
Because it’s purpose-built for the prompt boundary, Lakera doesn’t cover broader AI posture management or model supply chain risk, so teams need a separate tool for anything beyond screening inputs and outputs.
Cisco leans on its networking expertise to secure AI without requiring an agent or library inside the application itself. AI Defense examines AI traffic across the corporate network and applies policy in real time, detecting signs of prompt injection attacks, denial of service attempts, and data compliance violations.
What’s more, customers can pair Cisco AI Defense with Cisco Talos threat intelligence, which covers emerging threats against AI models and MCP endpoints, and use those insights to tune settings that determine which attack patterns should be blocked.
Because it runs through the existing Cisco Security Cloud framework, teams can extend governance policies they already have in place to AI traffic with minimal changes to underlying infrastructure.
Cisco AI Defense’s network-layer approach delivers the most value to organizations that are already running Cisco Security Cloud, so teams without that existing investment may find a purpose-built AI security vendor a more direct path to the same protection.
Wiz and Sysdig both started in cloud-native infrastructure monitoring, HiddenLayer began as model defense research, Lakera built its reputation through a public red-teaming game, and Cisco and Palo Alto came from the network layer. Different starting points, same conclusion: static checks aren’t enough to secure AI workloads, because it’s the live decision-making that matters most.
Because AI agents have access to third-party tools, documents, and the ability to act on enterprise systems, a compromised agent can cause far more damage than a chatbot giving a wrong answer ever could. That’s why security has to start where the threat actually begins, and with most production AI now living inside Kubernetes environments, extending existing runtime visibility to those workloads is the logical next step rather than a separate project.
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