How to Leverage AI Security to Protect Your Business in the Age of LLMs?

With AI tools taking over more of our lives, the question of security is starting to have more of an impact on personal and business endeavors. Businesses in particular have to deal with the risks associated with AI being used in their workflows. With these risks, it’s hard to find a tool set that won’t create additional risks. Luckily, solutions are starting to show up, with tools like nexos.ai taking over the market in order to increase security and efficiency of using AI in large organizations.

What is AI security?

AI security is security focused on protecting LLMs from various AI-specific threats. While traditional cybersecurity is a part of it, the way the models operate requires a different approach that includes AI-specific approaches, including prompt filtering, model safety, and data protection.

Why AI systems need their own security layer?

When thinking about cybersecurity, you usually consider the typical challenges. Avoiding brute force attacks, phishing campaigns, or code exploits becomes less important than safeguarding your model from prompt abuse.

This means the cybersecurity setup for AI is wholly different from traditional protection. Instead of predicting a person’s behavior, you need to predict a machine’s choices. Here are some of the primary differences between the two security layers:

Aspect	Traditional cybersecurity	AI security
Primary focus	Systems, networks, software protection	Model integrity, data confidentiality, AI behavior
Attack surface	IT infrastructure (OS, network, user)	AI models, training data, inference/prompt inputs
Typical threats	Malware, phishing, ransomware	Adversarial attacks, data poisoning, prompt abuse
Core defenses	Firewalls, antivirus, IAM	Model monitoring, prompt filtering, zero-trust AI

Key areas of AI and LLM security

Given the differences between traditional cybersecurity and AI security, I decided to take a deeper dive into some of the most important aspects of LLM safety measures. These include the way we approach prompts and outputs, data privacy, and platform safety.

Model security and safe outputs

A big example of how difficult controlling AI can be could be seen at the start of the AI craze, when LinkedIn was filled with jailbreaking guides for ChatGPT. The model didn’t want to execute your prompt? Tell it it’s a nostalgic story told by your grandma. Or that it’s for a sci-fi you’re writing.

While companies are getting better at protecting their LLMs from abuse, the arms race is on, and the jailbreaking methods are getting ever more creative. One of the newer jailbreaks required the user to fool the models into thinking they’re writing a House, M.D. episode in leetspeak.

Another vulnerability in the models is the data used to train AI, which can be a vector for attacks. If threat actors gain access to it, they’ll be able to insert false information and manipulate outcomes, creating outputs that can range from embarrassing to outright dangerous. For example, think about the dangers false medical information can cause in a hospital setting. Or if an LLM writing code injects a backdoor into code written for an unsuspecting newbie. This can have profound consequences if not treated correctly.

A lot of this can be prevented by using the right combination of input and output filtering, along with stringent dataset protections.

Data privacy

Breaking through prompt filters is only the tip of the iceberg. LLMs can be affected by various other breaches, which can have disastrous effects. A big issue is data privacy. AI models are fed petabytes of data daily by users, and a breach in the system could result in massive data leaks. That’s why it’s also important that AI users are aware of when their prompts are being used for training and when they’re not.

If user inputs are being used to train AI, they can be extracted by threat actors using a technique called a model inversion attack. This works by creating prompts that will trick the model into disclosing another user’s inputs.This means that a security system with precise permissions, roles, and access levels, as well as prompt monitoring, will be far safer for users who want to avoid having their data stolen through AI breaches.

Application-level security

Finally, application-level security is also crucial in terms of AI safety. This is the segment that has the most in common with traditional cybersecurity, as it requires models to mitigate various risks related to unauthorized access to models or APIs (application programming interfaces) used to communicate with them.

As more and more companies are switching to using LLM APIs for everything ranging from internal research to external chatbots, it’s important to secure the whole application setup end to end.

Another issue is how AI handles permissions. After all, you don’t want data leaking between customers using your chatbot and programmers using an LLM for building the website. Or, if multiple organizations use the same model, you don’t want information and tokens leaking between them due to the poor isolation of the model.

That’s why creating a robust solution that will protect the API and isolate the various access points from each other is crucial for your AI application’s security.

What to look for in an AI security platform?

Given all the threats that can be posed by using AI, choosing the right AI security platform is crucial if you want to keep your LLM and, by extension, your entire infrastructure safe. That's why it's important to consider a few key factors when picking an AI security platform:

Multi-model protection. It's important that your AI security tools have the tools required to protect multiple models in a variety of contexts.
Guardrails. The security platform you choose should have clear and enforceable policies. This will ensure your prompts and outputs are handled correctly within the model, preventing both prompt injection and data leakage.
Observability. Another important feature your chosen security model should have is observability. This should include prompt logs, model behavior monitoring, usage trends, and anomaly detection, allowing you to quickly identify and deal with any issues.
Data protection and access control. Ensure that the data connected to your AI system is secure and can only be accessed by authorized personnel. This includes both inputs like training data and prompts, and AI output like its responses or genAI content.
Regulatory compliance tools. As more jurisdictions introduce AI regulations along with existing privacy and copyright laws, it's important that the security software you choose features tools that will allow you to maintain regulatory compliance.

As you can see, the requirements for AI security platforms are rather robust and may even increase as AI use grows and more threats are discovered.

Platform	Number of supported models	Prompt filtering	Data redaction	Audit logs	Policy types	App/API security
nexos.ai	200+	✅ Real-time	✅ Full	✅ Full-stack	1000+ rules	✅ Multi-tenant
Lakera	~50	✅ Runtime only	❌	✅ Partial	~100	❌
Microsoft Purview	❌ (MS Azure AI only)	❌	✅ Data loss prevention focused	✅ Compliance	~300	❌
Robust Intelligence	~25	✅ Testing phase	✅ Partial	✅ Drift logs	~150	❌
CrowdStrike	Emerging LLMs	❌	✅ Partial	✅ Alerts	~50	✅ General apps

nexos.ai: secure AI orchestration with advanced features

Since nexos.ai is my top pick, I decided to dive deeper into the particular features that make nexos.ai the best choice for AI security. The tool, created by the team behind Nord Security, aims to deliver teams the best combination of AI security and convenience. Plus, for a limited time, you can test nexos.ai using a 14-day free trial.

As described in our nexos.ai review, the tool works primarily by providing a highly customizable browser-based workspace and API that relays information to over 200 LLMs. Through it, your application can route prompts to models appropriate for a given use case. After all, one model might be better for customer support chat, while another will be better for more technical tasks.

Luckily, nexos.ai doesn't sacrifice security for convenience's sake. Since all the model interactions go through its API, it can create a security framework that will ensure safety on both the input and output side. Here's how nexos.ai achieves this:

Full observability. Everything going on in your AI funnel is easily trackable through nexos’ tools, allowing users to detect any discrepancies.
Access control and policies. The AI orchestration suite allows you to set up custom policies, roles, and access control to ensure that only authorized personnel can access certain prompts or models.
Robust guardrails. nexos allows you to create customizable filters for all prompts and outputs in real time to block malicious prompts, prevent data leaks, and enforce policies.
Secure, shared workspaces. By creating separate workspaces, teams can safely cooperate with AI while maintaining policies for file uploads and prompts.
Intelligent caching. Combining security with convenience, nexos reduces the number of routed prompts by handling repeated or similar queries locally, thus reducing your team's spending and exposure.

Try nexos.ai free for 14 days

Use cases: AI security in action

To show you how AI security can be used, I decided to take a look at a few examples of real-life usage needs for AI from various sectors and how robust security tools like nexos.ai can help deal with these challenges.

AI data privacy in the financial and medical sectors

Even though finance and medicine may seem very distant, the data security challenges faced by both are pretty similar. Both are strictly regulated and deal with extremely sensitive data. Whether it is related to financial transactions or medical records, the danger of sensitive information leaking when using LLMs for analysis or assistance is significant.

Luckily, thanks to solutions that include automated data classification and real-time redaction, tools like nexos.ai can stop the information from leaking. When combined with encrypted, auditable data flows, you can ensure regulatory compliance without limiting your AI's capabilities.

AI security in SaaS integrations

With more and more SaaS tools integrating AI into their workflows for everything ranging from chatbots to document summaries, the risks of prompt injections rises. Without the right security measures, a threat actor can easily manipulate a model to gain access to sensitive data or disrupt workflows within a given solution.

The solution to these challenges is guardrails, which filter prompts and outputs to ensure that the information relayed between models is safe. Another element are role-based access controls, which help companies separate AI flows depending on access levels.

Accountability and governance

With more AI regulation being introduced around the world, being accountable for your model usage will become more important. This means that any AI tools you're going to use are going to have to handle compliance challenges.

Tools like nexos.ai handle this by providing users with robust auditing capabilities. This means that if a company is challenged by a regulator, it can easily prove its compliance while also reacting to any violations in real time. You can also read our nexos.ai vs Unify review to see how different providers handle security.

The cost of getting AI security wrong

The risks posed by weak AI security aren't imagined either. Over the past few years, we have already seen many different breaches occurring in the AI space, including some extreme examples from massive companies.

Data breaches

In 2023, AI was still a relatively new piece of software, and Samsung engineers used ChatGPT for troubleshooting some issues. Unfortunately, while doing so, they pasted sensitive information into the free version of ChatGPT, which then used it for training purposes. This resulted in proprietary code entering OpenAI's training data, exposing Samsung to corporate espionage among other problems.

Similarly, researchers found that LLM-based chatbots are a potential vector for attacks when they tested agents in the financial sector, managing to trick the customer support bots into leaking other users’ personal information.

These aren't isolated incidents, either. A Gartner survey showed that 30% of organizations using AI have suffered an AI-related security breach, which included primarily data leaks. What's more, a Financial Conduct Authority survey in the UK found that data leaks are the primary concern for most businesses using AI.

This could all be prevented by using tools offering AI guardrails and policies that would prevent the data from being used for training or shut down any prompts including proprietary code or private information altogether.

Regulatory violations

As lawmakers learn about the dangers of AI, new regulations emerge. The biggest package so far has been introduced by the European Union, with fines reaching up to €35 million or 7% of the company's global annual turnover (whichever is higher) for gross AI misconduct.

That is why it's important to set up policies that will prevent users within an organization from violating given rules. After all, one inattentive employee accidentally using AI to process biometric data could cost a company millions of dollars.Hence, the need for careful prompt monitoring, access control, and monitoring. This way, organizations will be able to not only ensure compliance but also prove it in case of an audit.

Conclusion: secure the future of AI

With the rise of AI use in various industries, the need for AI security increases as well. Data breaches, loss of customer trust, or regulatory fines can all result from improperly secured LLMs. That's why it's crucial you choose the right AI orchestration and security solution.

I believe the best choice for securing your organization's use of AI is nexos.ai . It combines intuitive and practical AI integrations through a browser-based workspace and API, with robust, all-in-one security solutions that will allow you to scale your AI work safely. And if you’re in doubt, you can now try nexos.ai with a 14-day free trial, available for a limited time only.

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AI security – how to protect models, data, and applications in the age of LLMs