The 5 best Robust Intelligence alternatives for AI security in 2025

If your company is using AI more and more, you’ve probably started thinking about the risks. Things like model failures, data poisoning, and weird attacks you’ve never heard of are becoming real concerns. It’s a bit like running a website without a firewall, you just wouldn’t do it for your critical apps, and the same thinking now applies to your AI models.

Platforms like Robust Intelligence popped up to handle these new threats, but the AI security world is getting crowded. It’s not just about protecting your backend models anymore. You also have to worry about the AI that actually interacts with your customers, making sure it’s safe and not saying wild things.

In this guide, we’ll break down five of the best Robust Intelligence alternatives. We’ll look at the tools that protect your models from technical attacks, but we’ll also cover a different, super important type of AI risk: making sure your customer-facing AI is actually helpful and not a liability.

What are AI security platforms and Robust Intelligence alternatives?

So, what exactly is an AI security platform? Just think of it as a cybersecurity suite, but built specifically for your AI and machine learning (ML) models. These platforms are designed to spot vulnerabilities, fight off attacks, and generally make sure your AI systems are doing what you actually want them to do, from the moment they’re being built to when they’re live in the wild.

Their main job is to handle the unique risks that come with AI, such as:

• Data Poisoning: This happens when someone intentionally feeds bad data into your model during its training phase, basically teaching it to make mistakes or creating a hidden backdoor.

• Evasion Attacks: Imagine an attacker making tiny, almost invisible changes to an image or a piece of text to fool your AI into making the wrong call. That’s an evasion attack.

• Model Theft: Yep, this is exactly what it sounds like. Bad actors trying to steal your hard-earned, proprietary model for their own use.

• Drift: This isn’t an attack, but it’s a huge risk. It’s when your model’s performance gets worse over time because the real-world data it’s seeing has changed since it was trained.

Basically, companies use these platforms to kick the tires on a model before it goes live and then keep an eye on it with real-time guardrails once it’s out there. It’s all about making sure the AI is effective, secure, and trustworthy, so you can avoid losing money, leaking data, or ending up on the front page for the wrong reasons.

How we evaluated the best Robust Intelligence alternatives

So, how did we pick these alternatives? We didn’t just pull names out of a hat. To make this comparison genuinely useful, we looked at a few specific things that matter for most teams.

Here’s what we looked for:

• Full Lifecycle Coverage: Does it protect the AI from training all the way to production? We wanted tools that cover the whole journey, not just one piece of it.

• Ease of Use & Integration: How much of a headache is it to set up? We gave extra points to tools that play nicely with existing MLOps and security setups without forcing you to rebuild everything.

• Threat Detection: How good is it at actually finding threats? This means spotting everything from common data poisoning attacks to brand new, sneaky vulnerabilities.

• Transparency and Control: When it flags a problem, does it tell you why? We looked for platforms that give you clear information and control over how you respond, not just a black box that makes decisions for you.

• Pricing: Is the pricing a giant mystery? Most enterprise tools make you sit through a demo to see a price tag, but we noted which ones are more upfront about their costs.

Quick comparison of the top Robust Intelligence alternatives

The 5 best Robust Intelligence alternatives in 2025

Here’s our detailed breakdown of the top platforms for managing AI risk and security.

1. eesel AI

Most AI security tools are all about protecting the model itself from hackers and technical failures. But eesel AI tackles a different, and arguably just as scary, risk: what happens when your AI has a bad day while talking to a customer? A support bot that confidently makes up a fake return policy or just can’t solve a simple problem can do some serious damage to your brand. eesel AI is built to stop that from happening, making it safe and surprisingly easy to use AI in your customer service.

It connects to the tools you already use, like your Zendesk or Freshdesk helpdesk, and your internal knowledge bases in places like Confluence. From there, it can automate support tickets, give your human agents a hand, or power a chatbot. It’s really designed for teams who need to solve this problem now, not after a six-month implementation project.

eesel AI connects with your existing tools, making it a flexible choice among Robust Intelligence alternatives for managing customer-facing AI risk.::

Why we chose it:

eesel AI is different because it focuses on what the AI does, not just how it’s built. The real killer feature for managing risk is its simulation mode. It lets you test-drive your AI on thousands of your past support tickets, so you can see exactly how it would have handled real customer questions. This lets you know how it’ll perform and where the weak spots are before you let it talk to a single live customer.

The simulation mode in eesel AI allows you to test performance on past tickets, a key feature for any team evaluating Robust Intelligence alternatives.::

Pros:

• You can get started in minutes. Seriously. It’s self-serve, so you can connect your helpdesk and have an AI copilot running in about five minutes. No waiting weeks for sales calls and onboarding.

• Test without the risk. The simulation mode lets you validate everything on old tickets first, which takes a lot of the stress out of launching something new.

• You’re in complete control. You decide which tickets the AI is allowed to touch and what it can do. You can start with something small and safe, and then let it do more as you get comfortable.

• The pricing is actually public. They have clear, public pricing plans. You pay for a certain number of interactions, not for every ticket it solves, so you don’t get punished for being successful.

Cons:

• It’s not designed to protect the backend model. It won’t scan for things like data poisoning or evasion attacks.

• It’s built for teams on the front lines, like customer support, sales, or IT, not so much for the MLOps and data science folks.

Pricing:

eesel AI keeps its pricing simple and public. All plans come with the core products like their AI Agent, Copilot, Triage, and Chatbot, and you can test it all out with a 7-day free trial.

• Team Plan: $299/month ($239/month if billed annually). This gets you up to 1,000 AI interactions per month, 3 bots, and integrations with sources like your website, docs, and Slack.

• Business Plan: $799/month ($639/month if billed annually). This bumps you up to 3,000 AI interactions per month, unlimited bots, and unlocks features like training on past tickets, AI triage actions, and bulk simulation.

• Custom Plan: If you’re a bigger company with needs for unlimited interactions, advanced security, or custom integrations, you’ll have to talk to sales.

One of the best parts is that you pay based on interactions, not resolutions. This means you won’t get a bigger bill just because your bot is doing a great job. You can check out all the details on their pricing page.

2. Protect AI

Now part of Palo Alto Networks, Protect AI is a heavy hitter in the world of MLSecOps (Machine Learning Security Operations). It’s a platform that aims to secure your entire machine learning supply chain, covering everything from the first line of code in a notebook all the way to the deployed model.

Their tools help you see all your ML assets in one place, "red team" your own models to find weaknesses, and spot vulnerabilities in the open-source components you’re using. This one is definitely built for the hardcore security and MLOps teams who need to know exactly what’s going on under the hood of their AI.

Why we chose it:

Protect AI made the list because it offers a really thorough platform for securing the entire ML pipeline. Its ability to scan not just your models but all their dependencies for known issues is something a lot of companies forget about until it’s too late.

Pros:

• Gives you a clear view of your whole ML pipeline.

• It’s great at sniffing out vulnerabilities in open-source libraries.

• Being part of Palo Alto Networks gives it a lot of cybersecurity credibility.

Cons:

• It can be a big project to set up and needs to be tightly integrated with your MLOps workflow.

• There’s no public pricing, so you have to go through the whole enterprise sales dance to figure out what it will cost.

Pricing:

You won’t find a pricing page on Protect AI’s website. You’ll have to contact their sales team, get a demo, and receive a custom quote. This is pretty standard for enterprise security software, but it makes it tough to compare options or even know if it’s in your budget without getting on the phone.

3. CalypsoAI

CalypsoAI is all about what they call "AI Trust, Risk, and Security Management" (AI TRiSM). The idea is to help companies validate, monitor, and secure their AI, especially the big language models (LLMs) everyone is using now. It basically works like a security checkpoint between your users and your AI, scanning everything that goes in and out for bad stuff like malicious prompts or sensitive data leaks.

It’s a popular choice for big companies, particularly in government and finance, that have to be super careful about making sure their use of AI models (whether built in-house or from providers like OpenAI) is secure and compliant.

Why we chose it:

CalypsoAI got on our radar because they were one of the first companies in this space and they’re laser-focused on securing LLMs, which is a huge deal right now. The fact that it can act like a firewall specifically for generative AI is what makes it stand out.

Pros:

• They’re experts when it comes to securing large language models.

• Their model validation and red-teaming tools are pretty powerful.

• The real-time guardrails are great for stopping data leaks or misuse as it happens.

Cons:

• It’s really built for huge companies with heavy compliance burdens, so it might be overkill for smaller teams.

• Setting it up can be complicated and you’ll likely need dedicated security folks to manage it.

• Again, no public pricing. You have to talk to sales to find out the cost.

Pricing:

No surprise here, CalypsoAI’s pricing isn’t public. To get a number, you’ll need to request a demo and get a custom quote from their sales team.

4. TrojAI

TrojAI’s main focus is protecting AI and ML models from what are known as adversarial attacks. Their platform is designed to defend against a whole menu of threats, from data poisoning and model evasion to attacks that try to pull sensitive information out of your models.

They do this with a one-two punch: heavy testing before you deploy a model, followed by a real-time firewall to protect it once it’s live. This makes it a good fit for companies in finance, healthcare, or any other field where you absolutely cannot have your model’s integrity compromised.

Why we chose it:

We picked TrojAI because of its deep expertise in adversarial attacks. While some other platforms do a little bit of everything, TrojAI really geeks out on finding and stopping the clever attacks designed to fool or break AI models.

Pros:

• They know their stuff when it comes to adversarial attacks.

• You get both penetration testing before launch and a firewall for when it’s live.

• Helps make your models tougher and less susceptible to being manipulated.

Cons:

• If you’re more worried about general AI governance than fending off super-sophisticated attacks, this might be too specialized for you.

• It’s another enterprise tool with mystery pricing, so budgeting is tricky without a sales call.

Pricing:

TrojAI’s pricing is only available if you ask for it. You’ll have to reach out to their sales team for a quote, as they mostly work with enterprise customers.

5. Cranium

Cranium is an AI security platform that helps companies get a handle on all their AI assets and make sure they’re secure and compliant. One of its neatest features is creating an AI "bill of materials," which is basically a list of every single component in your AI systems. This lets you map out, monitor, and manage AI risks across the whole company.

The platform is built to be easy to use and encourages collaboration, so your AI, security, and compliance teams can actually work together instead of in separate silos.

Why we chose it:

Cranium’s "bill of materials" idea is what landed it on our list. It’s such a simple, practical solution to a problem that a surprising number of companies have: they don’t even know all the AI they’re using. Cranium tackles that head-on.

Pros:

• It’s fantastic for just figuring out what AI you have and where.

• The interface is clean and easy to use, which helps with collaboration.

• It does a good job of getting AI developers and security folks on the same page.

Cons:

• It’s one of the newer kids on the block in this space.

• You guessed it, it’s an enterprise tool with no self-serve option or public pricing.

Pricing:

Cranium doesn’t post its prices online. To find out what it costs, you have to book a demo and talk to their sales team about a custom plan.

This video explores some of the most powerful and accessible AI tools available in 2025, providing context for the evolving landscape of AI applications.

How to choose the right Robust Intelligence alternatives for your business

Alright, so how do you pick the right platform out of this list? It really comes down to what you’re most worried about. Here’s a quick way to think about it:

• First, figure out your biggest risk. Are you losing sleep over hackers attacking your core ML models? Or is the bigger worry an employee accidentally pasting secret company data into a public chatbot? Or maybe it’s the nightmare of your new support bot giving terrible, brand-destroying advice to customers. Nail down your main problem first.

• If you’re on the MLOps or security team, you’re probably focused on the development lifecycle. In that case, check out tools like Protect AI or TrojAI. They’re built to plug into your pipelines and defend against technical attacks.

• If you’re on the compliance or governance team, especially in a regulated field, you need to worry about validating models and making sure LLMs are used correctly. CalypsoAI and Cranium are great for this, giving you the oversight you need.

• If you’re on a support or operations team, your main goal is to use AI safely with customers. A platform like eesel AI is going to be your best bet. It’s all about simulation, control, and ease of use to manage the risks of support automation.

Securing your AI with Robust Intelligence alternatives: From core models to customer conversations

At the end of the day, securing your AI isn’t just a "nice-to-have" anymore. While tools like Robust Intelligence and its competitors do a great job of protecting your core models from technical threats, that’s only half the battle. The other half, the part your customers actually see, is making sure the AI they interact with is reliable, accurate, and safe.

And that’s where a tool like eesel AI comes in. It adds that crucial layer of safety for your customer-facing AI. Because you can simulate everything first, control exactly what it does, and get it running in minutes, you can feel good about automating customer conversations without risking your reputation. When you pair solid backend security with a safe way to deploy AI on the front lines, you end up with an AI setup you can actually trust.

Ready to see how you can safely automate your support? Get started with eesel AI for free.