The Top 6 Platforms Securing AI Systems At Runtime

by
Alisa Davidson

Revealed: July 20, 2026 at 4:54 am Up to date: July 20, 2026 at 4:54 am

by Anastasiia O

Edited and fact-checked:
July 20, 2026 at 4:54 am

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Companies are now not simply utilizing AI chatbots to ask and reply questions. Lately, it’s all about autonomous AI brokers being unleashed inside company programs, studying by information and directories, making alterations to them, automating workflows by APIs and executing dwell code. 

Brokers are automating every little thing, they usually’re evolving as quick as they work. That’s why a static snapshot taken in the course of the growth course of is completely insufficient to defend these programs towards cyberattacks. Really, AI adoption within the enterprise has outrun the flexibility of most safety groups to observe what these programs really do as soon as they’re dwell.

Safety groups are studying the laborious manner that conventional static scanning instruments and pre-deployment configuration checks aren’t sufficient. In actual fact, one of the best ways to guard an AI system is to watch 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 house between a system passing assessment and a system working 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 definitely totally different from different kinds of software program safety, but it surely helps to know that the majority manufacturing AI now sits inside infrastructure that runtime safety has spent a decade studying to observe repeatedly.

On this article, we’ll check out six runtime safety choices that use very totally different strategies to maintain tabs on the dwell threats dealing with AI programs after they’re up and working in manufacturing.  

Agentless Safety’s Enlargement 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 deal with posture administration and configuration threat, connecting to cloud environments by read-only APIs to rapidly map a company’s footprint. That strategy is genuinely blind to what occurs throughout energetic execution, since a configuration snapshot can’t inform a staff whether or not a weak library is being exploited in reminiscence proper now.

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

Quite 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 high of the prevailing agentless basis somewhat than making an attempt 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 Home windows-specific model of Sensor, utilizing Home windows kernel telemetry somewhat than eBPF, extends the identical real-time monitoring to Home windows workloads. Collectively, the sensors preserve tabs on system processes, community connections, and file modifications working 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 fallacious elsewhere within the atmosphere. The one-two punch of agentless and eBPF lets safety groups see what’s taking place inside dwell AI fashions, app integrations and standalone brokers, making it simpler to actively block anomalous conduct, reminiscent of an LLM trying to exfiltrate delicate information with out authorization.

Not deployed mechanically throughout each workload, the runtime sensor is an add-on to Wiz, so it’s value 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 provides devoted runtime detection guidelines for coding brokers reminiscent of Claude Code, Gemini CLI, and Codex, constructed on Falco, the open supply runtime safety engine Sysdig created in 2016. The corporate 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 official 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 stage of telemetry finally feeds into Sysdig’s broader AI Workload Safety capabilities, which map dwell assault paths so groups can prioritize and reply to the alerts that matter most.

Alternatively, Sysdig’s AI-specific detections are much less mature than the corporate’s decade-old Kubernetes runtime engine, so organizations working important AI infrastructure on Home windows or exterior containerized environments might discover the protection thinner than what Sysdig provides for Linux-based workloads.

Palo Alto Networks Prisma AIRS

With Prisma AIRS, Palo Alto Networks gives risk safety throughout the AI growth lifecycle. The corporate’s AI Runtime Safety answer is the piece constructed for dwell manufacturing workloads, and it inspects software 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 Defend AI added a separate functionality, mannequin and provide chain scanning, somewhat than the runtime blocking itself. With Prisma AIRS 3.0, that runtime protection prolonged additional into agent-specific conduct, together with instrument misuse and reminiscence manipulation as autonomous brokers act in manufacturing.

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

As a result of AIRS was assembled from a number of acquisitions, together with Defend AI, consumers ought to anticipate some ongoing integration work as these underlying detection engines proceed to consolidate right into a single product.

HiddenLayer

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

The corporate 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 positive aspects helpful context on what baseline “regular” conduct seems to be like for a given mannequin, giving it indicators to validate fashions each earlier than and whereas they’re working in manufacturing.

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

Lakera Guard

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

Acquired by Examine Level in 2025, Lakera Guard sits between the consumer 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.

As a result of 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 instrument for something past screening inputs and outputs.

Cisco AI Protection

Cisco leans on its networking experience to safe AI with out requiring an agent or library inside the appliance itself. AI Protection 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 Protection with Cisco Talos risk intelligence, which covers rising threats towards AI fashions and MCP endpoints, and use these insights to tune settings that decide which assault patterns ought to be blocked. 

As a result of it runs by the prevailing Cisco Safety Cloud framework, groups can prolong governance insurance policies they have already got in place to AI visitors with minimal adjustments to underlying infrastructure.

Cisco AI Protection’s network-layer strategy delivers probably the most worth to organizations which are already working Cisco Safety Cloud, so groups with out that present funding might discover a purpose-built AI safety vendor a extra direct path to the identical safety.

AI Safety Operations Can’t Ignore Runtime Visibility

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

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

Alisa, a devoted journalist on the MPost, makes a speciality of crypto, AI, investments, and the expansive realm of Web3. With a eager eye for rising traits and applied sciences, she delivers complete protection to tell and interact readers within the ever-evolving panorama of digital finance.

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