The Top 6 Platforms Securing AI Systems At Runtime

Businesses are not simply utilizing AI chatbots to ask and reply questions. These days, it’s all about autonomous AI agents being unleashed inside company programs, studying by means of information and directories, making alterations to them, automating workflows by means of APIs and executing stay code. 

Agents are automating the whole lot, and so they’re evolving as quick as they work. That’s why a static snapshot taken through the growth course of is completely insufficient to defend these programs in opposition to cyberattacks. Truly, AI adoption within the enterprise has outrun the flexibility of most safety groups to look at what these programs really do as soon as they’re stay.

Security groups are studying the exhausting approach that conventional static scanning instruments and pre-deployment configuration checks will not be sufficient. In truth, one of the simplest ways to guard an AI system is to observe the environments that it lives in, because it operates. Pre-deployment checklists and one-time mannequin scans can’t inform a safety staff whether or not an agent is behaving usually at 2am on a Tuesday. That hole, the area between a system passing evaluate and a system operating unsupervised in manufacturing, is what runtime safety is constructed to shut.

As per the 2026 CNCF Annual Cloud Native Survey, 66% of organizations already internet hosting generative AI fashions run some or all of their inference workloads on Kubernetes. Securing AI programs, and their varied connections to different sources, is actually completely different from different forms of software program safety, however it helps to know that the majority manufacturing AI now sits inside infrastructure that runtime safety has spent a decade studying to look at constantly.

In this text, we’ll check out six runtime safety choices that use very completely different strategies to maintain tabs on the stay threats going through AI programs after they’re up and operating in manufacturing.  

Agentless Security’s Expansion to the Runtime Layer

Agentless instruments are broadly used for cloud safety immediately, however they didn’t begin out overlaying runtime in any respect. The earliest agentless instruments had a slender concentrate on posture administration and configuration threat, connecting to cloud environments by means of read-only APIs to rapidly map a corporation’s footprint. That strategy is genuinely blind to what occurs throughout lively execution, since a configuration snapshot can’t inform a staff whether or not a susceptible library is being exploited in reminiscence proper now.

That’s the hole that AI workloads have uncovered. Short-lived containers and brokers can solely be secured by observing what they’re doing throughout lively execution, and that requires runtime visibility that pure posture scanning was by no means constructed to offer. 

Rather than staying agentless-only, distributors like Wiz prolonged their platforms with a light-weight eBPF-based sensor constructed particularly for that job, including actual, agent-based runtime enforcement on prime of the present agentless basis fairly than attempting to stretch configuration scanning to cowl one thing it was by no means designed to do.

Wiz

As one of many pioneers of agentless cloud safety, it’s no shock that Wiz continues to innovate on the runtime layer with the Wiz Sensor. It’s constructed on eBPF, which permits kernel-level occasion monitoring on Linux hosts and Kubernetes nodes in actual time with out important useful resource overhead. 

A separate Windows-specific model of Sensor, utilizing Windows kernel telemetry fairly than eBPF, extends the identical real-time monitoring to Windows workloads. Together, the sensors maintain tabs on system processes, community connections, and file modifications operating inside containers and digital machines.

The thought is that the sensor reveals what’s taking place in actual time, whereas Wiz’s agentless posture administration reveals what else may go incorrect elsewhere within the atmosphere. The one-two punch of agentless and eBPF lets safety groups see what’s taking place inside stay AI fashions, app integrations and standalone brokers, making it simpler to actively block anomalous habits, similar to an LLM making an attempt to exfiltrate delicate information with out authorization.

Not deployed routinely throughout each workload, the runtime sensor is an add-on to Wiz, so it’s price remembering that organizations that don’t particularly configure and roll it out to their manufacturing programs are nonetheless solely getting agentless posture visibility, not real-time blocking.

Sysdig

Sysdig gives devoted runtime detection guidelines for coding brokers similar to Claude Code, Gemini CLI, and Codex, constructed on Falco, the open supply runtime safety engine Sysdig created in 2016. The firm has spent near a decade monitoring cloud-native runtime environments with Falco, and it leverages that have to increase protection to AI-specific workloads.

With Sysdig’s instruments, safety groups can floor indicators of compromise amid an ocean of professional agent exercise. For instance, a coding agent that tries to entry a delicate credential retailer will set off flags as a deviation from the norm. 

That degree of telemetry finally feeds into Sysdig’s broader AI Workload Security capabilities, which map stay assault paths so groups can prioritize and reply to the alerts that matter most.

On the opposite hand, Sysdig’s AI-specific detections are much less mature than the corporate’s decade-old Kubernetes runtime engine, so organizations operating important AI infrastructure on Windows or outdoors containerized environments could discover the protection thinner than what Sysdig gives for Linux-based workloads.

Palo Alto Networks Prisma AIRS

With Prisma AIRS, Palo Alto Networks offers menace safety throughout the AI growth lifecycle. The firm’s AI Runtime Security resolution is the piece constructed for stay manufacturing workloads, and it inspects utility visitors in actual time to dam immediate injection, information leaks, malicious code executions, and useful resource abuse. 

That runtime layer was constructed natively as a part of Prisma AIRS; the corporate’s 2025 acquisition of Protect AI added a separate functionality, mannequin and provide chain scanning, fairly than the runtime blocking itself. With Prisma AIRS 3.0, that runtime protection prolonged additional into agent-specific habits, together with software misuse and reminiscence manipulation as autonomous brokers act in manufacturing.

The firm is positioning Prisma AIRS as a single, built-in platform spanning runtime safety, provide chain scanning, mannequin safety, automated crimson teaming, and agent id, geared toward organizations that wish to keep away from stitching collectively fragmented level instruments.

Because AIRS was assembled from a number of acquisitions, together with Protect AI, patrons ought to count on some ongoing integration work as these underlying detection engines proceed to consolidate right into a single product.

HiddenLayer

HiddenLayer began as a analysis effort centered on mannequin tampering, manipulation, and mental property theft, earlier than rising right into a platform constructed to defend in opposition to those self same threats. That lineage reveals up in its runtime product, which watches stay mannequin habits for indicators of drift or adversarial inputs fairly than focusing totally on the immediate layer.

The firm constructed this on the identical basis as its provide chain scanner, which inspects mannequin information for hidden or malicious code very similar to antivirus software program inspects executables. 

By carrying that scrutiny into runtime, the platform good points helpful context on what baseline “regular” habits appears to be like like for a given mannequin, giving it indicators to validate fashions each earlier than and whereas they’re operating in manufacturing.

HiddenLayer’s concentrate on model-level protection means prompt-layer guardrails for chat-style purposes aren’t the platform’s core power, so groups constructing conversational AI options usually pair it with a devoted runtime guard fairly than counting on it alone.

Lakera Guard

Lakera Guard attracts on information from Gandalf, the general public sport the place gamers attempt to speak an AI mannequin into revealing a hidden password. Gandalf has attracted greater than 1,000,000 gamers worldwide, and the ensuing trove of adversarial prompts feeds instantly into the detection fashions behind Lakera Guard.

Acquired by Check Point in 2025, Lakera Guard sits between the person and the mannequin, inspecting each immediate and response to catch jailbreak makes an attempt, information leakage, and assaults embedded in retrieved paperwork or information. 

It’s constructed for sub-50 millisecond latency to reduce the hit on mannequin efficiency, and it really works with any LLM, no matter supplier, which makes it a pure match for monitoring customer-facing chatbots and AI assistants.

Because it’s purpose-built for the immediate boundary, Lakera doesn’t cowl broader AI posture administration or mannequin provide chain threat, so groups want a separate software for something past screening inputs and outputs.

Cisco AI Defense

Cisco leans on its networking experience to safe AI with out requiring an agent or library inside the appliance itself. AI Defense examines AI visitors throughout the company community and applies coverage in actual time, detecting indicators of immediate injection assaults, denial of service makes an attempt, and information compliance violations.

What’s extra, prospects can pair Cisco AI Defense with Cisco Talos menace intelligence, which covers rising threats in opposition to AI fashions and MCP endpoints, and use these insights to tune settings that decide which assault patterns ought to be blocked. 

Because it runs by means of the present Cisco Security Cloud framework, groups can lengthen governance insurance policies they have already got in place to AI visitors with minimal adjustments to underlying infrastructure.

Cisco AI Defense’s network-layer strategy delivers probably the most worth to organizations which can be already operating Cisco Security Cloud, so groups with out that present funding could discover a purpose-built AI safety vendor a extra direct path to the identical safety.

AI Security Operations Cannot Ignore Runtime Visibility

Wiz and Sysdig each began in cloud-native infrastructure monitoring, HiddenLayer started as mannequin protection analysis, Lakera constructed its repute by means of a public red-teaming sport, and Cisco and Palo Alto got here from the community layer. Different beginning factors, similar conclusion: static checks aren’t sufficient to safe AI workloads, as a result of it’s the stay decision-making that issues most.

Because AI brokers have entry to third-party instruments, paperwork, and the flexibility to behave on enterprise programs, a compromised agent could cause much more injury than a chatbot giving a incorrect reply ever may. That’s why safety has to start out the place the menace really begins, and with most manufacturing AI now dwelling inside Kubernetes environments, extending present runtime visibility to these workloads is the logical subsequent step fairly than a separate venture.

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