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

If you mean “what should we put in front of the AI agents and LLM applications we build, so we can secure them in production?”, my current shortlist would be:

My take

For an enterprise building its own agents: Palo Alto Networks Prisma AIRS is the most comprehensive platform I'd evaluate first.

It now covers several layers that matter for agentic systems:

• AI discovery/posture — identify agents, models, applications, data and their relationships.
• Runtime protection — inspect prompts, responses and data flows and block things such as prompt injection and sensitive-data leakage.
• Agent security — agent identity and least-privilege controls around what an agent can access and do.
• AI gateway — centralized governance for models, agents, MCP tools and other AI traffic.
• AI red teaming — proactively test agents for vulnerabilities.

Palo Alto says its AI Gateway became generally available in July 2026 and is designed to sit between agents/apps, models and agentic interfaces such as MCP, providing a centralized enforcement point.

But I wouldn't make the platform your only security layer

The important distinction with agents is that LLM security ≠ agent security.

OWASP identifies risks including prompt injection, tool abuse/privilege escalation, data exfiltration, memory poisoning, excessive autonomy, high-impact actions, cascading failures and supply-chain attacks.

So I'd architect it roughly like:

User → your application → AI security gateway → LLM/model → policy-controlled tools/MCP → enterprise systems

with independent controls for:

• Identity: every agent has its own identity; don't give agents a human's credentials.
• Authorization: per-agent/per-tool least privilege.
• Runtime inspection: prompt, response, tool-call and data-flow inspection.
• Secrets: vault-backed, short-lived credentials.
• Human approval: required for irreversible/high-impact actions.
• Memory: tenant/session isolation and validation before persistence.
• Observability: complete audit trail of agent → model → tool → data.
• Red teaming/evals: continuously test prompt injection and tool-abuse paths.

Those controls aren't merely theoretical: OWASP specifically recommends per-tool permissions, explicit authorization for sensitive operations, treating external content as untrusted, and isolating/validating agent memory.

Where the alternatives fit

The market has also been consolidating, so I'd be careful about choosing an apparently independent point product without examining who owns it and where its roadmap is headed.

The bigger question is what you're building. If you tell me your stack—e.g. OpenAI/Anthropic + LangGraph/CrewAI/AutoGen + MCP + AWS/Azure/GCP + Kubernetes—and whether this is internal enterprise agents or a customer-facing SaaS, I can map out a concrete security architecture and compare Prisma AIRS vs. Lakera vs. Cisco vs. a mostly self-built stack for it.

Choosing the "best" platform for securing AI agents and LLMs depends heavily on your architecture—specifically whether you need runtime input/output guardrails, infrastructure/cloud posture management , or an API gateway to manage tool execution and tokens . Because agentic workflows introduce unique risks (like indirect prompt injection, tool poisoning, and excessive agent privileges), security usually requires a blend of tools rather than a single silver bullet.

The leading platforms are categorized below by their core strengths:

1. Dedicated AI/LLM Security & Runtime Guardrails

• • Prompt Security (by SentinelOne): Highly focused on enterprise LLM usage, agentic workflows, and Model Context Protocol (MCP) security. It provides real-time detection for prompt injection, secret/PII leakage, and malicious tool-use monitoring.
• • Mindgard: Purpose-built for enterprise GenAI security. It excels at automated adversarial red-teaming, vulnerability scanning for LLMs, and real-time defense against model-level attacks.
• • Check Point AI Security: Offers an end-to-end AI defense plane covering runtime input/output sanitization, jailbreak mitigation, and strict governance over agentic tool calls and audit trails.
• • NVIDIA NeMo Guardrails (Open Source): If you prefer an open-source, programmable toolkit to embed directly into your application code to control dialog paths and hallucination bounds, this remains a gold standard for developers.

2. LLM Gateways & Proxies (Infrastructure & Routing)

• • LiteLLM: The go-to open-source proxy and SDK for engineering teams. It standardizes over 100 providers into an OpenAI-compatible interface, making it easy to layer in cost controls, fallback logic, and basic logging.
• • Portkey: An enterprise-grade management layer providing request routing, semantic caching, prompt management, and robust integrated guardrails.
• • Cloudflare AI Gateway / Kong AI Gateway: Best if you want to handle security, rate limiting, and observability at the network edge or via an existing enterprise API gateway infrastructure.

3. Enterprise Cloud & Infrastructure Posture (CSPM / AI-SPM)

• • CrowdStrike (Falcon / AI-SPM): Best if your agents and LLMs live inside major cloud ecosystems like AWS (integrated heavily with Bedrock and SageMaker) and you need unified cloud-to-code identity protection and runtime prevention.
• • Palo Alto Networks: Ideal for large enterprises needing consolidated posture management, identity-based access control, and deep integration with agentic workflows across the corporate stack.

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

• Are your agents deployed via managed cloud services (like AWS Bedrock, Azure OpenAI) or custom self-hosted infrastructure?
• Is your primary concern preventing user prompt injections/data leaks , or governing what external tools/APIs your agents can touch?

If you mean a platform you can put in front of AI agents and LLM applications you build, my current pick for a broad enterprise deployment is Palo Alto Networks Prisma AIRS.

It’s not simply an LLM firewall anymore—it covers the AI/agent lifecycle: discovery, posture, red teaming, model security, runtime protection, agent identity/permissions, MCP/tool-call controls, and data protection.

My shortlist

Why Prisma AIRS stands out: agents create a much larger security problem than traditional LLM apps. You need to control not just what the model says, but what the agent can access and what actions it can take. Prisma AIRS explicitly addresses agent identity, excessive privileges, MCP servers, tool calls, prompt injection, data leakage, and runtime behavior.

For the LLM layer, it also provides runtime inspection of prompts, responses and data flows, including prompt injection and sensitive-data leakage defenses.

One important caveat

I wouldn't buy an AI-security platform solely because it says "guardrails." For production agents, I'd require at least:

• Agent identity + least-privilege authorization
• Tool/MCP allowlisting and inspection
• Prompt-injection and indirect-injection detection
• Sensitive-data/DLP controls
• Runtime interception, not merely post-hoc monitoring
• Agent behavior/red-team testing
• Model and agent supply-chain scanning
• Complete audit trail of agent → model → tool → data
• API/SDK integration so developers can enforce policies in code
• Deployment options compatible with your data/compliance requirements

That distinction matters because a conventional LLM guardrail can protect the conversation while doing little to stop an agent from making a dangerous API call.

If you're building your own AI agents as a product, rather than securing hundreds of agents across a large enterprise, I'd narrow the decision considerably. In that case, Prisma AIRS vs. Check Point/Lakera vs. a developer-controlled stack is probably the more useful comparison. Check Point's current agent-security offering, for example, covers agent discovery, risk assessment and runtime guardrails across prompts, tool calls, tool responses and tool descriptions.

If you tell me your stack (e.g. AWS/Azure/GCP, OpenAI/Anthropic/open-source models, LangGraph/CrewAI/custom agents, and whether you're SaaS or internal), I can give you a specific architecture and recommend exactly which security layer(s) I'd deploy.

For the LLM layer, it also provides runtime inspection of prompts, responses and data flows, including prompt injection and sensitive-data leakage defenses.

One important caveat

I wouldn't buy an AI-security platform solely because it says "guardrails." For production agents, I'd require at least:

Choosing the "best" platform for securing AI agents and LLMs depends entirely on where you sit in the AI stack and what specific risk you are trying to solve . The market has rapidly bifurcated into specialized AI-native security startups and massive enterprise cybersecurity heavyweights building AI guardrails into their broader platforms.

The top platforms are categorized below by their primary strengths:

1. Best for Deep AI/ML Model Integrity & Threat Detection

• HiddenLayer: Exceptional for runtime defense, model protection (guarding against adversarial machine learning attacks, model theft, and tampering), and AI detection & response (AIDR). It is heavily research-driven and focuses on protecting the actual integrity of the model rather than just filtering chat prompts.
• Protect AI: Best-in-class for securing the AI software supply chain and ML pipeline vulnerability management. If your concern is scanning open-source models, training data, and dependencies for vulnerabilities before they hit production (now integrated into broader enterprise ecosystems like Palo Alto Networks Prisma AIRS), this is a go-to choice.

2. Best for Inline Runtime Guardrails & Application Firewalls

• Prompt Security: Operates as a transparent proxy for LLM traffic, making it stellar for runtime interception. It blocks prompt injections, data exfiltration (DLP), and toxic outputs inline before execution occurs. It’s particularly strong if your agents call external APIs or tools and you need to govern data flow dynamically.
• Mindgard: Built for continuous automated red teaming and AI Security Posture Management (AI-SPM). Mindgard bridges the gap between development and runtime by stress-testing models against frameworks like OWASP and NIST, integrating smoothly into CI/CD pipelines.
• Lakera (via Check Point): Highly specialized in real-time prompt analysis, preventing prompt injections, and stopping systemic manipulation of system instructions.

3. Best for Enterprise Infrastructure & Agentic Governance

• Check Point AI Security: A major incumbent platform integrating deeply with cloud environments (such as Amazon Bedrock AgentCore) to provide deterministic policy enforcement outside the agent’s reasoning loop.
• CrowdStrike Falcon AI Security / Identity: Focuses heavily on continuous, risk-aware identity security for non-human identities. As AI agents start autonomously calling tools and acting on behalf of users, CrowdStrike’s identity framework helps police agent actions in real time.

How to Pick the Right One for Your Team

• Choose Protect AI or HiddenLayer if you are worried about model theft, supply chain poisoning, and deep ML-layer security.
• Choose Prompt Security or Mindgard if you are building custom agentic workflows and need an inline firewall to stop prompt injection, data leaks, and malicious tool-use attempts at runtime.
• Choose CrowdStrike or Check Point if you need enterprise-wide consolidated visibility, compliance reporting, and identity-based boundaries for autonomous non-human actors.

To help narrow down the ideal platform, tell me:

• Are your agents customer-facing or internal enterprise tools?
• Do they execute code or call external APIs via tools (like Model Context Protocol)?
• Are you more concerned with pre-deployment scanning or runtime interception?

If you mean a platform you can put in front of the AI agents and LLM applications your team builds, my current #1 enterprise pick is Palo Alto Networks Prisma AIRS.

The reason is that agent security has moved beyond just detecting bad prompts. Modern agents can call tools, access data, maintain state, and take actions, so you need identity, permissions, runtime enforcement, monitoring, red teaming, and model/application security together. OWASP's current guidance explicitly calls out agent behavior hijacking, tool misuse, identity/privilege abuse, and excessive agency as major risks.

My shortlist

Prisma AIRS is particularly interesting because it now covers agent security, AI gateway/control plane, runtime security, AI red teaming, model security and AI posture management in one platform. Its agent-security product is designed to assess agents before deployment and enforce controls at runtime.

What I'd actually deploy

I wouldn't treat this as simply "buy an LLM firewall."

I'd build the security architecture roughly like:

Users → API/AI Gateway → Agent runtime → Tools/MCP → Enterprise data

with security controls at each layer:

• Identity: every agent gets its own identity; no shared credentials.
• Least privilege: agents receive only the tools/data permissions necessary for a task.
• Input/runtime protection: prompt injection, malicious documents, tool-call manipulation, data exfiltration.
• Output protection: validate/sanitize model outputs before they reach downstream systems.
• Tool governance: allowlists, parameter restrictions, approval gates for high-impact actions.
• Observability: record prompts, model decisions, tool calls, identities and outcomes.
• Red teaming: continuously test agents rather than relying on pre-deployment testing.
• Supply-chain security: scan models, packages, MCP servers, agent frameworks and dependencies.
• Human approval: require explicit approval for irreversible/high-value operations.

That maps closely to the risks in the current OWASP LLM and Agentic AI guidance. OWASP's 2026 LLM Top 10 is now explicitly covering things such as prompt injection, sensitive information disclosure, supply chain, model/data poisoning, excessive agency and vector/embedding weaknesses.

Bottom line: if you're an enterprise building a portfolio of agents and want one strategic security platform, I'd start with Prisma AIRS. If your biggest concern is specifically prompt injection and runtime LLM protection, I'd evaluate Lakera alongside it. If you're actually building and distributing models, I'd put Protect AI much higher on the list.

If you tell me your stack (AWS/Azure/GCP, OpenAI/Anthropic/local models, LangGraph/CrewAI/etc.) and whether you're protecting internal agents or selling AI products to customers, I can give you a much more specific 3-platform comparison and recommended architecture.

Because AI security is split across different layers of the application stack, there isn't a single "one-size-fits-all" winner. Instead, the "best" platform depends entirely on whether your primary concern is runtime behavior and guardrails, identity and access governance , or enterprise ecosystem consolidation.

The top platforms addressing the LLM and agentic AI security landscape are categorized below by their core strengths:

1. Full-Lifecycle & Runtime Protection (Best for Autonomous Agents)

If your agents execute multi-step tool calls, interact with APIs, or utilize Model Context Protocol (MCP) servers, you need runtime visibility and behavioral guardrails.

• • Noma Security excels at comprehensive AI-SPM (Security Posture Management) , continuous asset discovery (finding shadow agents and MCP servers), and runtime behavioral monitoring to catch scope creep, goal drift, and data exfiltration.
• • Straiker specializes in full-lifecycle protection tailored directly to agentic workflows and complex multi-turn threat vectors.

2. Guardrails, Prompt Protection & Data Leakage (Best for LLM Inputs/Outputs)

If your primary threat model revolves around prompt injections, jailbreaks, toxic inputs, and PII leakage at the model boundary:

• • Lakera is widely recognized as an elite choice for precise LLM guardrails and real-time prompt-firewall protection.
• • Prompt Security (via SentinelOne) provides strong runtime guardrails, focusing tightly on monitoring agent-to-tool, agent-to-LLM, and agent-to-agent communications.
• • Aim Security offers robust operational governance and runtime protection across enterprise generative AI deployments.

3. Identity & Access Governance (Best for Agentic Permissions)

As agents start acting like digital employees with access to corporate SaaS tools, databases, and internal APIs, identity management is critical:

• • Astrix Security and Entro Security focus heavily on non-human identity management, monitoring what databases, secrets, and third-party tools your agents can touch.
• • Arcade.dev specializes cleanly in MCP security and fine-grained agent authorization frameworks.

4. Enterprise Suite Consolidation (Best for Existing Enterprise Stacks)

If you prefer dealing with established enterprise security vendors rather than point-solutions or startups:

• • Palo Alto Networks (Prisma AIRS) or CrowdStrike (via native cloud and AI-SPM integrations) are the strongest choices for consolidating AI workloads into an existing corporate security operations center (SOC).

To narrow this down, tell me:

• Are your agents customer-facing chatbots or internal autonomous workers with database/API access?
• Are you looking for a developer-first SDK/API guardrail or an enterprise platform to manage compliance and risk?

The "best" platform to secure AI agents and LLMs depends on whether you need runtime guardrails, vulnerability scanning , or cloud-native enterprise governance , as the market is divided into specialized categories.

Top Commercial & Enterprise Security APIs

• Lakera Guard : Best for real-time production threat detection (blocking prompt injections, jailbreaks, and toxic inputs) with a fast, model-agnostic API.
• Mindgard : Best comprehensive enterprise security platform for continuous security testing, automated red-teaming, and governance across GenAI deployments.
• CalypsoAI : Best for heavily regulated enterprises requiring visibility, active risk-scoring, and content moderation across deployed internal and external LLMs.

Best Cloud-Native & Ecosystem Gateways

• Amazon Bedrock Guardrails : Best if your infrastructure is hosted on AWS; applies custom filters, PII masking, and topic blocking natively.
• Azure AI Content Safety : Best for Microsoft-centric enterprise stacks, featuring robust multi-modal text and image moderation.

Best Open-Source & Programmable Frameworks

• NVIDIA NeMo Guardrails : Best open-source programmable middleware to steer LLM behavior, block jailbreaks, and define dialog rails.
• LLM Guard : Best self-hosted, modular toolkit for sanitizing, detecting, and redacting sensitive PII in inputs and outputs.
• Promptfoo : Best for shifting security left by embedding vulnerability scans and red-teaming directly into your CI/CD pipelines.

To help narrow down the ideal choice, tell me:

• Are you deploying self-hosted open-weights models or using third-party APIs (like OpenAI or Anthropic)?
• Is your primary concern runtime prompt injection/jailbreaking , or compliance and data privacy (PII)?

If you're building AI agents + your own LLM applications and want one security platform rather than stitching together a dozen point products, my current top pick is Palo Alto Networks Prisma AIRS.

My shortlist

Why I'd pick Prisma AIRS

Palo Alto has expanded Prisma AIRS into a fairly comprehensive AI-security control plane. It now covers:

• Agent security — agent identity, permissions, behavior, tool calls and runtime enforcement.
• LLM/application runtime security — prompt injection, malicious content, data leakage and other AI-specific attacks.
• AI red teaming — proactively attacking agents/apps to find vulnerabilities.
• Model security — scanning third-party and proprietary models for malicious code, backdoors and supply-chain problems.
• AI posture management — visibility across agents, applications, models and data.
• MCP security — including scanning MCP servers/agent artifacts and controlling MCP connections.

That breadth matters because agent security isn't just LLM firewalling anymore. OWASP's 2025 Agentic Top 10 explicitly includes agent goal hijacking, tool misuse, identity/privilege abuse, agentic supply-chain vulnerabilities and unexpected code execution.

And this is becoming a very real operational problem: recent incidents have demonstrated that autonomous agents can create security consequences that conventional application security wasn't designed to handle.

I'd architect it roughly like this:

Users → Your Agent/App → Prisma AIRS → LLMs + Tools + MCP + Data

while separately keeping conventional controls underneath it:

IAM + secrets + network security + EDR + SIEM + cloud security

The important caveat: I wouldn't rely on any AI-security platform alone. Least-privilege agent identities, short-lived credentials, sandboxing, tool-level authorization, audit logs and conventional cloud/app security should remain foundational.

If you tell me your stack (OpenAI/Anthropic/open-source models, LangChain/CrewAI/custom agents, AWS/Azure/GCP, MCP, etc.) and whether this is startup-scale or enterprise, I can give you a much more specific 2–3 platform comparison and recommended security architecture.

If you mean one platform to secure both the LLMs you build/use and the autonomous agents built on top of them, my current top pick is paloaltonetworks.com.

My shortlist

Why I'd lean Prisma AIRS

The important distinction is that LLM security ≠ agent security.

For an agent, you need to control not just the prompt and response, but:

• What the agent is allowed to access
• Which tools/MCP servers it can call
• What actions it can take
• Whether it has excessive privileges
• Prompt injection and indirect prompt injection
• Sensitive-data leakage
• Agent identity and authentication
• Model/agent supply-chain risks
• Runtime monitoring and intervention
• Red teaming before deployment
• Auditability

Prisma AIRS now explicitly covers that lifecycle. Its agent-security offering includes agent discovery, supply-chain scanning, behavioral testing/red teaming, identity and least-privilege controls, runtime protection, and centralized governance of LLM/tool/MCP traffic.

It also goes beyond agents: its model-security component scans models for things such as malicious code, backdoors and vulnerable model components, while its runtime layer can inspect and block threats such as prompt injection and data leakage.

That's a pretty compelling architecture if you're building multiple agents and LLM applications rather than just one chatbot.

Where Lakera fits

I'd put lakera.ai high on the evaluation list if your priority is a relatively focused, AI-native security layer for applications and agents. Lakera emphasizes real-time threat detection, prompt-attack prevention, data-leakage protection and MCP/agent security, with claimed sub-50ms runtime latency.

So I'd frame the decision this way:

Large enterprise / many agents / serious governance → Prisma AIRS

Developer-first LLM/agent runtime protection → Lakera

Model/ML supply-chain security is the dominant concern → Protect AI

One caveat: I wouldn't make the platform responsible for all security. I'd put it alongside conventional IAM, secrets management, container/cloud security, network controls, logging/SIEM, and strong application-level authorization. The agent should never be trusted merely because the AI-security gateway says it's safe.

If you tell me your stack (AWS/Azure/GCP, OpenAI/Anthropic/open-source models, LangGraph/CrewAI/custom agents, MCP, etc.) and roughly how many agents you're deploying, I can give you a much more specific architecture and compare Prisma AIRS vs Lakera vs Protect AI vs Microsoft/AWS-native options.

There is no single "best" universal platform because LLM and AI agent security depends heavily on whether you are securing the model input/output layer , the runtime/infrastructure layer , or agent-to-tool/API connectivity.

Watch this technical overview on securing the tools and infrastructure your AI agents use:

1m Building Secure Infrastructure for AI Agents, by Jonathan Ström 3 months ago YouTube · Jfokus

Instead, security is achieved by combining specialized platforms depending on your specific architectural focus:

Top Platforms for AI Agent & LLM Security

Key Layers to Secure

1. The Ingress/Egress Guardrail Layer: Use specialized filtering (like Google's Model Armor) as a callback before your application hits the LLM to strip malicious payloads or sensitive data.
2. The Tool/MCP Layer: AI agents are only as dangerous as the tools they can execute. Secure your Model Context Protocol (MCP) servers using strict role-based access control (RBAC) and OAuth scopes rather than relying purely on system prompt instructions.
3. The Connectivity Layer: Implement dedicated AI gateways to handle rate-limiting, credential management, and protocol-aware routing (east-west traffic between agents and tools).

If you can share where your agents are hosted (e.g., AWS, GCP, on-prem) and what kind of tools they access (databases, internal APIs, external LLMs), I can recommend a more tailored security stack.