What's the best platform for securing the AI agents and LLMs we build?

Because the security paradigm has shifted rapidly from simple static text generation to autonomous agentic workflows (where AI can execute tools, call external APIs, and make multi-step decisions), there is no single "silver bullet" platform.

Instead, the "best" choice depends heavily on where your risk lies: input/output validation, infrastructure runtime execution, or enterprise perimeter governance. The leading platforms and tools are categorized by their core strengths:

1. Dedicated LLM & Agentic Runtime Firewalls (Best for Real-Time Traffic & Guardrails)

If you need to block prompt injections, jailbreaks, PII leaks, and toxic outputs dynamically at runtime before they reach your LLM or return from your agent:

• Lakera / Prompt Security / Lakera Guard: Excellent for enterprise-grade real-time prevention of indirect prompt injection and data leakage.
• Check Point AI Security: A robust choice if you are looking to secure AI usage, shadow AI, and governance across a broader corporate infrastructure.
• Fortinet (FortiAIGate): Purpose-built as an enterprise LLM security gateway if your infrastructure leans heavily on traditional network security fabric integration.

2. Developer-First Frameworks & Output Validators (Best for Python / App Stacks)

If you are building custom agents in code and need granular control over inputs, structured output validation, and hallucinations:

• Guardrails AI: Exceptional for validating, filtering, and enforcing structural/type constraints on LLM outputs inside a code-heavy stack.
• NVIDIA NeMo Guardrails: Great for programmatically defining conversational boundaries and application-specific topical guardrails.
• LLM Guard (Open Source/Commercial): A comprehensive toolkit for sanitizing inputs, scanning for secrets/PII, and evaluating model outputs.

3. CI/CD Testing & Red Teaming (Best for Pre-Production Safety)

Securing an agent means testing it for vulnerabilities before it ever touches production:

• Promptfoo: The gold standard for CI/CD-integrated LLM testing, regression testing, and catching prompt injection flaws during development.
• Garak: Widely used as the "nmap for LLMs" for automated vulnerability scanning and red teaming.

4. Infrastructure & Workload Runtime Security (Best for Autonomous Agents)

If your agents have access to local tools, containers, and server infrastructure:

• Oligo Security: Specialized in runtime application and library security specifically tailored for AI and ML workloads.
• EdgeLabs: Excellent for self-hosted, air-gapped, or edge AI workloads requiring strict runtime detection without cloud dependencies.

To help narrow down the ideal stack, tell me:

• Are your AI agents self-hosted/on-premise or running on managed cloud APIs (like OpenAI, Anthropic, or AWS Bedrock)?
• Are they performing autonomous actions/API tool calls , or are they strictly user-facing chat interfaces?

If you're asking “what should we standardize on to secure the AI agents and LLM applications we build?”, my current top pick is Palo Alto Networks Prisma AIRS—especially if you're building enterprise-grade agents that can call tools, access data, or take actions.

My shortlist

The reason I put Prisma AIRS first is that the problem has moved beyond “filter bad prompts.” Modern agents need security around identity, permissions, tool calls, MCP, data access, runtime behavior, model supply chain, and prompt injection. Palo Alto's current platform explicitly covers those areas, including agent discovery, agent identity, least-privilege controls, runtime enforcement, MCP/tool-call monitoring, red teaming, and model security.

That's increasingly important because OWASP's 2025/26 guidance identifies agent behavior hijacking, tool misuse, and identity/privilege abuse as major agentic risks—not merely traditional LLM prompt vulnerabilities.

What I'd actually build

I wouldn't make the security platform the only security layer. I'd use:

Your agents → AI security gateway/runtime → models + tools + enterprise data

with:

• Agent identity + least privilege
• Prompt-injection detection
• Tool/MCP-call authorization
• Sensitive-data/DLP inspection
• Model and dependency scanning
• Runtime behavioral monitoring
• Red teaming/evals before deployment
• Complete audit trail
• Conventional IAM, network segmentation, secrets management, and endpoint/cloud security underneath it

That architecture matters because a compromised agent can potentially turn a benign prompt injection into an authorized tool action. Recent real-world incidents involving AI coding agents demonstrate that this isn't merely theoretical.

If you tell me what you're building (e.g. customer-facing SaaS agents, internal enterprise agents, coding agents, healthcare/finance, RAG apps, or your own LLMs), I can give you a specific 3–5 product architecture and tell you which platform I'd buy vs. build ourselves.

I wouldn't make the security platform the only security layer. I'd use:

Your agents → AI security gateway/runtime → models + tools + enterprise data

with:

There is no single "best" platform because the right choice depends heavily on where your agents live, how they are built , and your existing security stack . Securing agentic workflows requires coverage across runtime behavior, prompt firewalls, supply chain/MCP (Model Context Protocol) security, and posture management.

The top platforms are categorized below by what they do best:

1. Dedicated & Specialized AI Security Startups (Best for Deep, Native Agent & LLM Protection)

• • Noma Security : Excellent for full lifecycle protection covering homegrown AI, autonomous agents, data sources, and MCP servers with deep runtime policy enforcement.
• • Zenity : The go-to choice if you need to discover shadow AI/agents across enterprise platforms and enforce inline, intent-based governance at runtime.
• • NeuralTrust : Strong for modular AI security—offering distinct layers for continuous adversarial red teaming, AI gateways, and runtime behavioral guardrails.
• • Lakera : Best-in-class for real-time runtime protection against prompt injections, jailbreaks, and data leakage, now integrated into broader enterprise ecosystems.

2. Major Enterprise Cyber Platforms (Best if You Want Unified/Consolidated Security)

• • CrowdStrike Falcon : Ideal if you are already in the CrowdStrike ecosystem; extends Falcon to operationalize and secure agentic AI workloads and identity policies.
• • SentinelOne Singularity : Great for folding AI runtime, Model Context Protocol (MCP) protection, and agentic workflow monitoring directly into existing endpoint and cloud workload security.
• • Palo Alto Networks : Best for large enterprises looking to consolidate AI security, access management, and agentic workflow guardrails under a single strategic enterprise umbrella.

3. Developer & Infrastructure-Focused Tools

• • EdgeLabs : Exceptional if you are running self-hosted, air-gapped, or on-premises Kubernetes LLM workloads where local runtime detection without cloud dependencies is required.
• • WitnessAI : Strong for acting as an enterprise AI firewall and governing how applications and employees interact with external LLMs.

To help narrow down the ideal platform for your team, tell me:

• Are you building custom agents from scratch (e.g., Python/LangChain/LlamaIndex) or using low-code enterprise agent builders (e.g., Copilot Studio, Salesforce)?
• Are your LLMs self-hosted/open-source or accessed via API (OpenAI, Anthropic, Bedrock)?

If you mean one platform to secure AI agents + LLM applications + the models themselves, my current pick is Palo Alto Networks Prisma AIRS.

It has moved beyond being just an LLM guardrail product: Prisma AIRS now covers agent discovery/posture, runtime protection, AI gateways, red teaming, model security, and AI data/application security in one platform.

My shortlist

Why I'd lean Prisma AIRS

For an organization actually building agents, I'd want security at several layers:

• Before deployment: scan models, applications and agents; red-team them.
• At runtime: inspect prompts, responses, tool calls and tool responses.
• Agent governance: know which agents exist, what tools/MCP servers they can reach, and what they're authorized to do.
• Data protection: prevent sensitive information from leaking through prompts, outputs or tools.
• Network/cloud layer: contain compromised agents and AI workloads.
• Auditability: centralized policies, logging and enforcement.

Prisma AIRS now explicitly addresses essentially this whole lifecycle. Its AI Runtime API can inspect prompts/model responses programmatically, while its runtime firewall protects AI traffic and its model-security component scans models for vulnerabilities before deployment.

The particularly interesting part for agentic AI is that Palo Alto has added an AI Gateway/control plane and agent-security capabilities around identity, permissions, tool calls and runtime behavior.

The main competitor I'd evaluate

Check Point AI Agent Security (formerly Lakera) is probably the one I'd put through a bake-off against Prisma AIRS.

Its strength is being very focused on AI-native runtime security. It can discover agents and their MCP connections, assess agent risk, and apply guardrails across prompts, model outputs, tool calls, tool responses and tool descriptions.

So I'd frame the decision this way:

If you're an enterprise security organization: → Prisma AIRS

If you're a product/AI engineering team that primarily wants an API-first security layer around agents and LLM applications: → Check Point AI Agent Security / Lakera

If you're primarily worried about third-party/open-source model integrity and model supply-chain attacks: → I'd investigate HiddenLayer alongside the above rather than treating runtime guardrails as sufficient.

One important caveat: no AI-security platform replaces the application's authorization model. Your agents should still operate with narrowly scoped identities, least-privilege tool permissions, isolated execution, explicit approval for consequential actions, and strong secrets management. The security platform should be a defense-in-depth control plane, not the thing that makes an otherwise overprivileged agent safe.

If you tell me what you're building (e.g. SaaS agents, internal enterprise agents, coding agents, RAG apps, autonomous agents, or your own/fine-tuned models), I can give you a much more specific recommendation and a Prisma AIRS vs. Lakera vs. HiddenLayer comparison with architecture, deployment model, latency, coverage, and likely cost.

If you mean “what platform should we standardize on to secure AI agents + LLM applications from development through production?”, my current pick is Palo Alto Networks Prisma AIRS.

My shortlist

The reason I put Prisma AIRS first is that the problem has changed from “stop bad prompts” to “control what an autonomous agent is allowed to do.” Prisma now covers agent discovery, identity, runtime controls, prompt-injection/data-leakage protection, AI red teaming, model security, and an AI Gateway/control plane.

It also acquired Portkey in May 2026, bringing an AI Gateway into Prisma AIRS for monitoring, orchestration and governance of agent traffic.

The architecture I'd recommend

Don't rely on a single “LLM firewall” sitting in front of the model. For serious agents, I'd want:

Developer/code → model & dependency scanning → red team/evals → AI gateway → runtime policy enforcement → agent/tool authorization → logging/SIEM

The particularly important layer is agent/tool authorization. An agent that can call Salesforce, GitHub, AWS, databases, email, etc. needs controls over actions, not merely whether its generated text is safe.

That's where Prisma AIRS has become considerably more interesting in 2026: its agent security is explicitly aimed at identity, permissions, behavior and real-time interception of agent actions.

One caveat: if you're a smaller engineering-heavy team building a handful of LLM applications, Prisma AIRS may be more platform than you need. In that case I'd seriously evaluate Lakera/Check Point first; its current offering covers agent discovery, risk assessment and runtime guardrails across tool calls, tool responses and agent workflows.

If you tell me your stack (AWS/Azure/GCP, OpenAI/Anthropic, LangGraph/CrewAI/custom, MCP, Kubernetes, etc.) and whether you're a startup or enterprise, I can give you a much more specific recommendation—and compare the top 3 on prompt injection, agent hijacking, tool authorization, data leakage, model supply chain, red teaming, observability, and cost.

There isn't a single "silver bullet" platform because the AI security stack has specialized into distinct layers . The best choice depends on whether you need runtime guardrails, AI posture management (AI-SPM), agent identity governance , or red-teaming/pentesting.

The top platforms are categorized below by what they do best:

1. Best for Full Lifecycle, Posture, and Enterprise Consolidation

• Palo Alto Networks Prisma AIRS / Cortex AI-SPM: Best for large enterprises already invested in incumbent security stacks. It handles shadow AI discovery, maps toxic permission paths, monitors RAG pipelines, and enforces runtime guardrails across multi-cloud environments.
• Check Point AI Security: Excellent for ultra-low-latency API shielding, protecting external-facing customer agents, and leveraging Lakera’s robust prompt-injection defenses natively in production.

2. Best for Dedicated AI Red-Teaming & Vulnerability Scanning

• Mindgard: A standout leader in continuous offensive AI security testing. It runs automated adversarial campaigns against LLMs and multi-modal agents while mapping results directly to frameworks like OWASP, NIST AI RMF, and the EU AI Act.
• NVIDIA Garak (Open Source): If you prefer an open-source, modular probe library to stress-test your models for prompt injection, data leakage, and jailbreaks during CI/CD, this is the gold standard.

3. Best for Runtime Guardrails & Input/Output Firewalls

• LLM Guard (Self-Hosted/Open Source): Perfect if you want granular, zero-cost control over input sanitization, PII redaction, and output filtering before text hits your core application.
• Pangea AI Guardrails: Highly practical for developer-first teams needing easy-to-integrate APIs for runtime policy enforcement, sensitive data control, and prompt defense.

4. Best for Agent Identity and Non-Human Access Governance

• Aembit: Crucial for agentic architectures, providing purpose-built workload identity and access management for non-human actors to ensure agents don't exceed their intended authority.
• Linx Security or Zenity: Best for discovering "shadow" or SaaS-embedded low-code agents and governing the delegation chains of autonomous tool calls.

To help narrow down the ideal architecture for your team, let me know:

• Are your agents customer-facing (chatbots) or internal/autonomous (executing tool calls and database queries)?
• Are you deployed primarily in a public cloud (AWS, Azure, GCP) or require on-prem/air-gapped controls?

There is no single "best" platform because securing AI agents and LLMs requires covering three distinct layers: model/gateway routing, runtime guardrails (the AI firewall) , and agent-to-tool/MCP (Model Context Protocol) authorization.

The top platforms are categorized below by where they excel in the security stack:

• Bifrost (by Maxim AI) / Portkey : Best for high-performance AI gateways and runtime proxy routing . They unify multi-provider LLM access, add microsecond-level latency overhead, manage virtual consumer keys, and natively tie into downstream guardrails (AWS Bedrock, Azure Content Safety, Google Model Armor) while locking down MCP tool permissions.
• NeuralTrust : Best for large-scale enterprise production governance . It provides a full lifecycle approach (from pre-deployment red teaming to runtime guardrails and continuous posture tracking) with a split-plane architecture allowing data planes to live strictly inside your private cloud or on-premises environment.
• General Analysis : Best for agent-to-tool and adaptive attack-path mapping . It excels at analyzing the boundaries of complex multi-agent architectures—mapping prompts, retrieval/RAG sources, permissions, and MCP servers to run adaptive red-teaming campaigns.
• Palo Alto Networks Prisma AIRS : Best for incumbent enterprise security stack consolidation . Following major capability additions (including elements from Protect AI), it offers robust shadow-AI discovery, posture management, model scanning, and runtime firewall controls tailored for autonomous agents.
• Galileo / Giskard : Best for evaluation-driven defense, tracing, and automated regression testing against the OWASP Top 10 for GenAI . They map failure patterns across distributed multi-agent traces and handle rigorous pre-deployment red teaming.
• EdgeLabs / Prediction Guard : Best for self-hosted, on-premises, or air-gapped sovereign environments . They enforce zero-trust runtime policy governance, PII masking, and sandboxing directly inside your own infrastructure without relying on an external cloud security SaaS backend.

To help narrow down which platform fits your architecture, tell me:

• Are your agents self-hosted/on-prem or running on public cloud/SaaS APIs?
• Do your agents call external tools or MCP servers , or are they strictly conversational?

If you mean a platform to secure AI agents and LLM applications you build yourselves—not just an enterprise AI governance product—my current pick is Palo Alto Networks’ Prisma AIRS for a serious production/enterprise deployment.

The important caveat: there isn't one platform that's best at every layer. The security stack should cover model/supply-chain security, application/agent security, runtime enforcement, identity/permissions, and red teaming.

My shortlist

Why I'd lean Prisma AIRS

The big distinction with agents is that "LLM security" isn't enough anymore. Your agent can receive a prompt injection, call an MCP server, retrieve poisoned content, access a database, send data somewhere, or take an irreversible action. Recent security research and incidents are emphasizing that agents need to be treated more like privileged identities than ordinary chatbots.

Prisma AIRS 3.0 now covers unusually broad territory:

• Agent discovery and inventory
• Agent identity and permissions
• Agent behavior testing/red teaming
• Prompt-injection and malicious-content detection
• Runtime monitoring and enforcement
• MCP/tool-call security
• Data-loss protection
• Model/supply-chain scanning
• AI posture management

Palo Alto explicitly positions it as covering the AI lifecycle from development through runtime, rather than merely putting a filter in front of an LLM.

That breadth matters if you're building multiple agents rather than one chatbot.

But I'd seriously evaluate Lakera too

Check Point AI Security / Lakera is particularly interesting if your engineering team wants something API-first and relatively unobtrusive.

Its runtime layer analyzes prompts, model outputs, tool calls, tool responses and tool descriptions, with defenses for prompt injection, data leakage, malicious links and agent behavior.

Lakera also claims sub-50ms runtime latency and supports SaaS or self-hosted deployment, which can be attractive when you're putting security directly into an application's request path.

So I'd frame it:

Prisma AIRS = security platform/control plane

Lakera = excellent AI-native runtime security layer

One thing I would not do

I wouldn't rely on an LLM firewall/guardrail alone.

For an agent that can actually do things, I'd architect roughly:

User → Agent gateway → AI security/runtime policy → LLM → tool/MCP authorization → external systems

with:

Identity + least privilege + secrets isolation + sandboxing + audit logs + red teaming

around it.

The most important control is actually what the agent is permitted to do, not simply whether its prompt looks malicious. Prisma AIRS is moving toward this model with agent identity, permissions and real-time action controls.

If I were choosing for a new AI company

I'd run a bake-off between Prisma AIRS and Lakera using your actual agents, not generic benchmark prompts.

Test at least:

1. Direct prompt injection
2. Indirect injection from webpages/PDFs
3. Malicious MCP/tool descriptions
4. Tool-call manipulation
5. Data exfiltration
6. Credential/secret extraction
7. Excessive agent permissions
8. Memory poisoning
9. Cross-agent attacks
10. Malicious model/package supply chain
11. Latency and false-positive rate
12. Ability to block an unsafe action, rather than merely flag it

The memory-poisoning issue is especially worth testing now because persistent agent memory creates an attack surface that conventional LLM guardrails don't necessarily address.

Bottom line: If you want one strategic platform for securing both the LLMs and autonomous agents you're building, I'd start with Prisma AIRS. If you're primarily looking for a developer-centric runtime security API that you can embed into your own AI stack, I'd put Lakera at the top of the evaluation.

If you tell me your stack (AWS/Azure/GCP, OpenAI/Anthropic/open-source models, LangGraph/CrewAI/custom, MCP, etc.) and whether you're SaaS or enterprise, I can narrow this to the best 2–3 options and show you what the architecture would look like.

If you're building your own AI agents and LLM applications, rather than merely securing employees' use of ChatGPT, my current top pick is Palo Alto Networks Prisma AIRS.

It has moved beyond simple prompt filtering into a broader AI-security platform covering AI applications, models, data, and agents, including runtime protection, red teaming, posture management, and agent security. Its 3.0 release specifically targets autonomous agents and their ability to execute actions.

My shortlist

HiddenLayer is the one I'd evaluate most seriously against Prisma AIRS. Its current Agentic Runtime Security specifically reconstructs multi-turn agent sessions and tool calls, detects prompt injection/jailbreaks and unsafe tool use, and can enforce policies at runtime. Its platform also covers AI discovery, supply-chain security, attack simulation, and runtime security.

The important architectural point is that you don't want to secure just the LLM. Once an agent can call tools, access databases, read documents, invoke APIs, use MCP, or modify systems, the highest-impact security boundary becomes the agent execution layer. Recent research likewise emphasizes that agent security needs to address multi-step tool interactions and state, not just input/output filtering.

What I'd deploy

For a serious production environment, I'd think about the stack as:

1. Model/supply-chain security
→ Scan models, dependencies, agent skills/MCP servers, containers, etc.

2. Application/LLM security
→ Prompt injection, jailbreaks, sensitive-data leakage, unsafe outputs.

3. Agent runtime security — most important
→ Identity, least privilege, tool authorization, action policies, runtime monitoring, anomaly detection, human approval for high-impact actions.

4. Continuous red teaming
→ Attack the whole agent, not merely the underlying model.

5. Conventional security underneath it all
→ IAM, secrets management, network segmentation, EDR, cloud security, logging/SIEM, DLP.

That's why I'd favor Prisma AIRS or HiddenLayer over a standalone "LLM guardrails" product if you're building agents that can actually take consequential actions.

My recommendation:

• Large enterprise / want one strategic security vendor: Prisma AIRS
• AI-native engineering team / want particularly deep agent runtime visibility: HiddenLayer
• Primarily concerned with model provenance and ML supply chain: Protect AI
• Early-stage application and mostly LLM input/output controls: Lakera or a lighter guardrail layer

If you tell me your architecture (e.g. OpenAI/Anthropic + LangGraph/CrewAI + AWS/Azure/GCP + MCP/tools) and roughly how many agents you're deploying, I can give you a much more concrete "buy X, configure Y, don't bother with Z" security architecture.

There is no single "best" platform because securing AI agents and LLMs requires coverage across different operational layers—from code and data pipelines to runtime tool execution and identity management.

The top platforms are categorized below by what layer of the stack they protect best:

• For Runtime Guardrails & Real-Time Firewalls (Best for filtering prompt injections, jailbreaks, and PII leaks at the API layer):
  • Lakera Guard / Check Point AI Security : Excellent for low-latency, real-time input/output shielding on user-facing applications and chatbots.
  • Galileo : Highly effective if you need a unified workspace for hallucination detection, evaluation, and inline guardrails.
  • NVIDIA NeMo Guardrails : The go-to open-source option if you prefer programmable, policy-as-code safety rules.
• For AI Gateways & Traffic Routing (Best for managing multi-model architectures, rate-limiting, and Model Context Protocol (MCP) governance):
  • Bifrost : A high-performance, open-source gateway combining LLM, MCP, and agent routing with minimal latency overhead.
  • Kong AI Gateway : Ideal if your infrastructure already leverages Kong for traditional API management and you need native PII masking and prompt guarding.
  • Hyperscaler options like Amazon Bedrock Guardrails or Google Cloud Model Armor if you are fully locked into a specific cloud ecosystem.
• For AI Security Posture Management (AI-SPM) & DevSecOps (Best for discovering shadow AI, scanning codebases, and generating AI Bills of Materials - AI-BOMs):
  • Snyk : Outstanding developer-first tooling that spans IDE analysis, AI component discovery, and autonomous remediation.
  • Mend.io : Strong emphasis on tracking RAG pipelines, MCPs, and supply-chain vulnerabilities across the software development lifecycle.
  • Wiz or Noma : Great for broad enterprise posture management, visibility, and risk correlation across complex cloud environments.
• For Agentic Identity & Access Governance (Best for controlling what tools, APIs, and databases autonomous agents are allowed to touch):
  • Astrix Security or Linx Security : Purpose-built to treat AI agents as non-human identities (NHIs), enforcing least-privilege permissions and blocking unauthorized privilege escalation.

To help narrow down the ideal choice, tell me:

• Are your agents customer-facing chatbots or internal autonomous workers with database/tool access?
• Are you deploying in the cloud (AWS/GCP/Azure) or self-hosted/on-prem?