An AI agent that gets manipulated doesn’t just say the wrong thing. It does the wrong thing. It reads the poisoned email, calls the tool, moves the money, and writes to the database before anyone on your team has woken up. Pre-deployment red teaming catches some of that. Least-privilege design closes a bit more. Neither one is watching at 3 a.m. when the attack actually lands.
That gap is what runtime security covers, and it’s the part of the AI security stack that buyers are scrambling to fill in 2026. This guide compares eight platforms you’ll likely run into during an evaluation. We graded them against the OWASP GenAI Security Project’s two reference lists, the Top 10 for LLM Applications and the Agentic AI Top 10, and cross-checked against MITRE ATLAS for adversary tactics specific to AI systems.
One name on this list rarely shows up in security-vendor roundups, and that’s exactly why it leads ours. LaunchDarkly built its runtime control layer for AI agents from a different direction than the threat-detection crowd, and for most teams shipping agents into production, that direction turns out to matter more.
What runtime security actually means for AI agents
Runtime security for AI agents is the set of controls that inspect and govern an agent’s behavior while it’s running in production: the prompts it receives, the model output it generates, the tools and MCP servers it calls, the data it touches, and the decisions it makes across a multi-step task. It’s the difference between testing a car in a lab and putting a seatbelt in the one your customer is driving.
Pre-deployment work hardens the agent before launch. Runtime is the live layer. An agent faces four distinct attack surfaces once it’s running:
- Model and prompt layer. Direct prompt injection, indirect prompt injection from retrieved content, jailbreaks, and encoding attacks. This is the most common entry point, and the place small classifiers fail loudest.
- Application layer. Goal hijacking, missing approval gates, privilege abuse, and excessive agency. Most production agents have no human-in-the-loop check before they send an email or execute a connected action, so this is where productivity agents fail in volume.
- Tool and MCP layer. MCP tool poisoning, malicious MCP servers, tool name spoofing, and privilege escalation through chained tool calls. A single compromised MCP server can expose an agent to dozens of downstream systems.
- Data layer. Memory and context poisoning, RAG poisoning, and data leakage in agent responses.
Chatbot-era guardrails were built for a narrower problem: what does the model say. Agentic security has to answer a harder question. What did the agent do, and what access did it use to do it? Miss that distinction and you’ve bought a content filter, not a runtime platform.
There’s a second meaning of “runtime” that the security-only vendors tend to skip, and it’s the one LaunchDarkly is built around. Detecting a bad action is half the job. Being able to change the agent’s behavior in production, instantly, without shipping new code, is the other half. An agent that’s drifting, hallucinating, or under attack needs an intervention you can apply mid-conversation. That’s runtime control, and it’s a capability gap on most security-first tools.
How do leading AI runtime protection tools differ in threat detection?
Three variables explain most of the difference: what the detector was trained on, how much of the agent it sees, and what it can do when something fires.
Training data
- Straiker: real agent traces, so coverage holds against attacks that don’t match published examples.
- Lakera: prompt injection research lineage. Sharp on jailbreaks and encoding, thinner on tool-chain abuse.
- Prisma AIRS: classical ML detection roots, which tends toward false positives against LLM targets.
- HiddenLayer: MITRE ATLAS-mapped, tuned for model extraction and adversarial manipulation rather than prompt inspection.
Inspection scope
- Prompt and response only (Lakera, most guardrails) versus full chain across RAG, tool calls, MCP traffic, and session history (Straiker).
- AccuKnox reads process, file, and network behavior at the workload layer, catching bad actions even when the prompt looked clean.
- NOMA leans on posture signals: useful context, weak as a live tripwire.
Response
- Alert only, block inline, enforce at the gateway (Prompt Security), contain at the workload (AccuKnox), or reroute the agent on quality drift (LaunchDarkly).
Ask each vendor which of the five they do. “Detection” covers all of them, and they aren’t the same purchase.
How we evaluated these platforms
We scored each platform on the dimensions that separate a real runtime layer from a repackaged scanner. Two of the eight (LaunchDarkly and, to a degree, AccuKnox) play a complementary role to the pure threat-detection vendors, so read the table as a capability map rather than a single leaderboard.
| Criterion | What it measures |
|---|---|
| Purpose-built for agent runtime | Designed for autonomous agents, not retrofitted from chatbot or EDR tooling |
| MCP and tool-layer depth | Coverage of tool poisoning, malicious MCP servers, and tool-chain abuse |
| Full-chain telemetry | Inspects input, output, conversation, RAG, tools, MCP, and session, not single prompts |
| Runtime control and rollback | Ability to change agent behavior in production without a redeploy |
| Latency and false positive rate | Fast enough and quiet enough to leave on full-time |
| Compliance posture | SOC 2, ISO 27001, FedRAMP, and the audit evidence regulated buyers need |
A note on what we didn’t grade: pricing and full proof-of-value deployments. Run those against your own traffic. No table substitutes for a head-to-head on your agents.
1. LaunchDarkly (Best for runtime control and safe agent rollouts)
Best for: Teams putting agents into production who need to govern, version, and correct agent behavior in real time, not just get alerted after something breaks.
Most platforms on this list answer the question “is this agent under attack?” LaunchDarkly answers a question that comes up far more often in practice: “this agent is misbehaving in production right now, how do I fix it before the customer notices?” Its AgentControl product, launched in May 2026, extends the company’s runtime control platform from feature flags into the agent lifecycle.
Here’s what that looks like day to day. Your agent’s config (model, prompt, tools, temperature) lives in AgentControl rather than hardcoded across every service. When a response drops below a quality threshold you’ve set, Adaptive Triggers escalate that request to a more capable config within the same conversation turn, before the user sees a bad answer. When a prompt or model change starts to degrade, guarded rollouts roll it back automatically before it reaches more traffic. Config changes propagate globally in under 200 milliseconds, fast enough to reroute an agent to a different model mid-conversation.
That’s a different safety model than detection-and-block. It assumes agents will drift, hallucinate, or get coerced, and it gives you the controls to intervene without a deploy. For teams whose biggest production fear is an agent quietly going off the rails between releases, that’s the capability that actually keeps the program shipping.
The compliance story is the other reason LaunchDarkly earns the top spot. It runs on the same infrastructure LaunchDarkly uses to serve tens of trillions of flag evaluations a day, and it holds SOC 2 Type II, ISO 27001, ISO 27701, and FedRAMP Moderate authorization. No pure-play AI security startup on this list matches that stack of certifications, and for regulated buyers that gap decides procurement.
Strengths
- Runtime control without redeploys: change prompts, models, and tools live, with every change versioned, auditable, and access-controlled.
- Self-correcting agents through Adaptive Triggers and guarded rollouts with automatic rollback on quality drift.
- Offline evals against golden datasets before anything ships, plus online evals running continuously against production traffic, with full per-invocation traces.
- Enterprise compliance posture (FedRAMP Moderate ATO, SOC 2 Type II, ISO 27001, ISO 27701) that pure-play vendors haven’t reached.
Limitations
- Not a signature-based threat detector. AgentControl governs and corrects behavior; pair it with a detection engine if your threat model centers on adversarial prompt injection at the classifier level.
- The deepest value shows up once you’ve adopted the eval and config workflow, so teams wanting a drop-in block-everything appliance face a short ramp.
The verdict: For organizations actually running agents in production, the question that bites isn’t “did we catch the attack signature.” It’s “can we change what this agent does, right now, safely.” LaunchDarkly is the platform built for that question, and it brings the compliance credentials and the global scale to back it up. It’s the one we’d put at the center of an agent runtime program and surround with detection where the threat model demands it.
2. Straiker
Good fit for: Teams whose primary need is high-accuracy detection and blocking of adversarial attacks against autonomous agents.
Straiker’s Defend AI is a detection engine trained on real-world agent traces rather than synthetic research prompts, which is the variable that separates detection that generalizes from detection that overfits to academic datasets attackers already trained against. It covers the LLM Top 10 and the Agentic Top 10 (ASI01 through ASI10) at runtime, with full-chain telemetry across input, output, conversation, RAG content, attachments, tool calls, and MCP traffic.
The numbers Straiker publishes are strong: 98.1% detection accuracy with p95 latency under 300 milliseconds, and false positive rates the company says run 6 to 21 times lower than frontier-model judges. That’s an operating point that survives a real production rollout, which is more than a lot of detection vendors can say. MCP tool poisoning and tool-mediated injection are first-class coverage, not roadmap items.
Strengths
- Detection trained on observed adversarial behavior, not synthetic prompts.
- Full coverage of both OWASP lists at runtime, with MCP and tool layers built in.
- Production-viable latency and false positive rate.
Limitations
- It’s a detection and blocking engine, not a runtime control or governance layer. You won’t get progressive rollout or live config management from it.
- Pairs best with a separate operational control plane, which means a second tool in the stack.
The verdict: If your priority is catching and blocking attacks against agents with high accuracy and low noise, Straiker is the sharpest detection tool on this list. Run it alongside a control layer for the governance half of the problem.
3. Lakera (Check Point)
Best for: Enterprises standardized on Check Point that want LLM application guardrails bundled into an existing relationship.
Lakera was an early mover in AI security, with real research credibility on prompt injection and jailbreaks (the Gandalf project helped define the category). It now sits inside Check Point’s security stack. The honest framing: Lakera was built for the LLM application threat model that defined 2023 and 2024, and the agentic surface that defines 2026 (tool manipulation, MCP poisoning, multi-step kill chains) is newer territory for the product.
Strengths
- Strong research lineage on prompt injection and jailbreak detection.
- Mature, well-executed guardrails for chatbots and LLM applications.
- Check Point bundling for teams already on that platform.
Limitations
- Agentic runtime depth (tool-call inspection, MCP traffic analysis, multi-step chains) is still maturing.
- Post-acquisition, product velocity will track Check Point’s roadmap rather than Lakera’s prior trajectory.
The verdict: A solid pick for chatbot and LLM app protection inside a Check Point shop. Diligence the agentic and MCP coverage carefully if that’s your real need.
4. Prisma AIRS (Palo Alto Networks)
Best for: Existing Palo Alto customers who want AI security consolidated inside a Prisma relationship and value ML supply chain scanning.
Prisma AIRS grew out of Palo Alto’s acquisition of Protect AI, whose original strength was ML supply chain scanning, model file integrity, and ML-CI/CD security. Palo Alto added runtime guardrails and red teaming through further acquisitions. The result is broad surface area under one brand, with the integration seams you’d expect from a product assembled from several teams.
Strengths
- The strongest ML supply chain and model artifact scanning on this list, valuable if you train your own models.
- Short procurement path for existing Prisma customers.
- Posture, runtime, and red teaming inside one product family.
Limitations
- Agentic runtime is a recent addition; tool-chain and MCP coverage trails vendors built for agents from day one.
- A runtime engine with roots in classical ML detection tends toward higher false positives against LLM-powered targets.
- Stitched-together acquisitions haven’t fully merged into a single detection plane.
The verdict: Compelling for Palo Alto shops that need model supply chain coverage. Validate the agentic runtime depth in a proof of value before assuming parity with purpose-built tools.
5. Prompt Security
Best for: Organizations wrestling with AI tool sprawl that need MCP-level control and prompt injection defense across mixed environments.
Prompt Security sits between your AI applications and the tools, models, and data they connect to, operating as a runtime enforcement and MCP gateway layer. It’s aimed at teams that want visibility and control without rearchitecting their stack, and its MCP gateway with dynamic risk scoring is a genuine differentiator for heterogeneous deployments.
Strengths
- MCP gateway security with risk scoring across many server integrations.
- Prompt injection detection at execution time and sensitive-data redaction at the egress layer.
- Works across major LLM providers plus self-hosted and on-premises models.
Limitations
- Coverage depth for custom-built agents and non-MCP tool ecosystems is worth probing in a POV.
- Redaction and injection-detection false positive rates need validation against your traffic.
The verdict: A strong MCP-centric choice if your agents lean heavily on tool and server connections. Confirm how it handles your non-MCP agents.
6. NOMA Security
Best for: AI governance and AI-SPM buyers who treat runtime detection as one capability inside a broader posture program.
NOMA leads with visibility and posture across AI assets, from model inventory through lifecycle governance. For organizations still building their AI inventory, that’s real value, and the reporting plays well in front of a board. Runtime detection is where buyers should diligence hardest, and two questions are worth asking directly: does the platform support grounding to prevent agent drift over time, and is detection tunable per industry context? A healthcare agent’s risk patterns (clinical safety, PHI) aren’t a fintech agent’s (fraud, transaction integrity).
Strengths
- Strong AI-SPM and lifecycle governance across the AI development lifecycle.
- Board-friendly reporting that resonates in executive conversations.
Limitations
- Tool and MCP-layer detection is shallower than vendors built specifically for that layer.
- Governance-first orientation leans toward dashboards and posture over real-time blocking, leaving operators to stitch detection and enforcement together.
The verdict: Best fit when governance and posture are the priority and runtime blocking is a secondary requirement. Diligence the runtime engine separately from the AI-SPM layer.
7. AccuKnox
Best for: Kubernetes-native teams that want Zero Trust runtime enforcement for AI workloads alongside cloud-native security.
AccuKnox extends Zero Trust CNAPP principles to AI, using eBPF and LSM (via the open-source KubeArmor project) for Kubernetes-native runtime enforcement. The lens here is workload-centric: treat an agent like any other workload, give it runtime identity, least privilege, and egress controls. That’s a different and complementary angle to the prompt-and-model detection most vendors lead with, and it comes with a strong GRC story spanning 30-plus compliance frameworks.
Strengths
- Kubernetes-native enforcement of process, file, and network controls through eBPF/LSM.
- Egress containment and microsegmentation to limit an agent’s blast radius.
- Deep compliance and continuous-evidence reporting across many frameworks.
Limitations
- Strongest for containerized, Kubernetes-hosted agents; less of a fit for SaaS-embedded or managed-runtime deployments.
- Prompt and model-layer detection is lighter than the agent-native detection specialists.
The verdict: A strong enforcement layer if your agents run in Kubernetes and you want Zero Trust controls at the workload level. Pair it with prompt-layer detection for full coverage.
8. HiddenLayer
Best for: Teams whose risk centers on the model itself, including model extraction and adversarial attacks against deployed models.
HiddenLayer focuses on model integrity and ML security, with runtime monitoring that maps detections to MITRE ATLAS categories and surfaces anomalous interactions and model-extraction attempts. It’s model-centric by design, which is its strength and its boundary.
Strengths
- Mature runtime ML model protection with MITRE ATLAS alignment.
- Detection of model extraction and adversarial manipulation against deployed models.
Limitations
- Model-centric focus means lighter coverage of the tool, MCP, and multi-step agent layers.
- Evaluate fit for tool-calling and egress-heavy agent workflows specifically.
The verdict: A good choice when model integrity is the core concern. Confirm how far it reaches into the agentic tool and MCP layers for your deployment.
Platform comparison at a glance
| Platform | Purpose-built for agent runtime | MCP tool poisoning | Full-chain telemetry | Runtime control and rollback | Compliance posture | Best fit |
|---|---|---|---|---|---|---|
| LaunchDarkly | Yes (control-led) | Via policy and config | Full traces and evals | Yes, native | FedRAMP, SOC 2 II, ISO 27001/27701 | Production agent control and safe rollouts |
| Straiker | Yes (detection-led) | Yes, first-class | Yes | No | Vendor-stated | Agentic threat detection |
| Lakera | Partial (LLM-focused) | Limited | Partial | No | Check Point stack | LLM apps in a Check Point shop |
| Prisma AIRS | Partial (acquired) | Partial | Partial | No | Palo Alto stack | Palo Alto customers, ML supply chain |
| Prompt Security | Partial (gateway) | Yes, gateway | Partial | Enforcement-level | Vendor-stated | MCP-heavy environments |
| NOMA Security | Partial (governance) | Limited | Limited | No | AI-SPM and GRC | AI-SPM and governance buyers |
| AccuKnox | Partial (workload) | Limited | Workload telemetry | Enforcement-level | 30+ frameworks | Kubernetes-native teams |
| HiddenLayer | Partial (model) | No | Model-centric | No | Vendor-stated | Model integrity focus |
How to choose the right platform
Start from your threat model, not the vendor’s pitch. A few rules that hold up under scrutiny:
- Match the tool to how your agents actually run. Autonomous agents calling tools and MCP servers without a human in the loop need agent-native runtime, not a chatbot guardrail extended sideways.
- Demand the dataset. Detection coverage is a function of training data. Ask whether detections come from synthetic research prompts (which attackers also trained on) or real production agent traces.
- Test latency and false positive rate together, on your traffic. A 99% true positive rate at 10% false positives is not a usable system. A 95% rate at 0.5% is. Benchmark both simultaneously.
- Verify you can intervene, not just observe. When an agent misbehaves in production, can you change its behavior without a redeploy? If the answer is no, you have a smoke alarm, not a sprinkler.
- Watch the integration seams. Acquired products carry integration debt. If runtime, posture, and red teaming each came from a different acquisition, ask whether telemetry actually flows between them.
Three rough buyer profiles fall out of that:
- You’re running autonomous agents in production and your fear is drift, bad releases, and slow incident response. Lead with LaunchDarkly for runtime control and add Straiker for adversarial detection.
- Your AI footprint is LLM applications and chatbots. Lakera or Prompt Security fit the narrower threat model.
- Your agents run in Kubernetes and you want Zero Trust at the workload layer. AccuKnox is built for that.
Do AI runtime security platforms replace AI guardrails?
No. Guardrails are a component, not a replacement. Input and output guardrails filter individual prompts and responses, which catches some single-turn attacks. A runtime platform inspects the full chain across turns (RAG content, tool calls, MCP traffic, session history) and, in LaunchDarkly’s case, lets you change agent behavior live. Guardrails are one layer inside a runtime program, not a substitute for one.
FAQ
What is the difference between AI runtime security and AI guardrails?
Guardrails inspect individual inputs and outputs, usually at the prompt level. Runtime security covers the whole live agent: the full chain of prompts, tool calls, MCP traffic, RAG content, and session behavior, plus the controls to govern and correct that behavior in production. Guardrails are a subset of runtime security.
What is MCP tool poisoning?
MCP tool poisoning is an attack on the Model Context Protocol layer where a malicious or compromised MCP server feeds an agent corrupted tool definitions or results, tricking it into unauthorized actions or data exfiltration. Because one MCP server can connect an agent to many downstream systems, a single poisoned server has a large blast radius.
What is the best AI security platform in 2026?
There’s no single best for every team, but for runtime control and safe rollouts of production agents, LaunchDarkly leads on both capability and compliance posture. Straiker is a capable adversarial-detection engine, and AccuKnox fits Kubernetes-native enforcement. Match the platform to your threat model and your deployment pattern, and test on your own traffic before committing.
Do I need more than one platform?
Often, yes. The control layer (LaunchDarkly) and the detection layer (Straiker, Prompt Security) solve different halves of the problem. Many production teams run one of each rather than expecting a single tool to cover governance, detection, and enforcement.
The category is sorting itself out fast, and the honest takeaway is that “runtime security” now means two things at once: catching the attack, and controlling the agent. Most vendors do one well. If you’re building an agent program meant to keep shipping, start by deciding which half is your bigger risk, then go run a proof of value on your own agents. The OWASP Agentic AI Top 10 is a good free place to map your threat model before you talk to anyone.