The business world’s embrace of AI means tech decision-makers are faced with a brand new problem: the rate and variety of AI tool adoption may outpace their management efforts. AI gateways position themselves as a holistic solution that provides greater visibility into and control over evolving AI environments.
You don’t have to grasp the technology's technical intricacies to appreciate its worth. However, you do have to understand why AI gateways are gaining traction and how they address the risks of unchecked AI adoption.
What Are AI Gateways?
As AI tools evolve and adoption inside companies grows, so does the likelihood of fragmentation. Employees in general use Gemini or ChatGPT to summarize documents, developers might run and test local models, while sales teams leverage specialized AI agents to autonomously update CRMs or send out email chains.
This is where the best AI gateways come in as a layer that sits between applications employees use and the actual AI systems the applications interact with.
The gateway itself isn’t a bottleneck. Rather, it’s a unified point of contact for all AI tools that lets companies apply security rules and see what’s happening. Think of AI gateways as digital versions of security checkpoints. Requests still reach their destinations, but now the organization has a consistent place where it can check what employees are accessing, enforce rules, and intervene if something goes wrong.
Why Are AI Gateways Gaining Attention?
Alone, the high-level explanation above doesn’t adequately explain why it's in tech decision-makers’ best interest to implement top AI gateways. To that end, here are several compelling reasons that might not be apparent right away.
They help measure AI spend
The more model calls teams and applications start to make, the harder it becomes to keep track of AI spend. Gateways act as usage aggregators in this case, surfacing the applications that consume most AI resources, which models cost the most, and whether all usage is truly necessary.
They reduce dependence on individual AI providers
Gateways make routing between providers easier, even if doing so might otherwise have required extra development work on existing applications. Being able to use different models for different workloads is invaluable for speed and cost reduction. Meanwhile, the ability to switch to another model if the default one is offline or experiencing issues helps maintain business continuity.
They support AI experimentation
AI models evolve at a brisk pace. It might only be a matter of months before a faster, more capable one supplants the once state-of-the-art models you continue to use. Gateways make experimentation with new models easier, more responsible, and safer. They eliminate the time-consuming task of rebuilding integrations, giving teams more incentive to test new models and compare their performance. This also reduces the likelihood and impact of shadow AI.
Their value increases the more AI is connected to tools
Basic AI integration was limited in that an application could only interact with an AI and then do something with the output. For example, a customer inquires about a package delay; the CS chatbot they interact with contacts ChatGPT and posts the reply it provided without resolving anything.
The transition to agentic workflows sees applications calling on other tools as well. In the example above, an agent could access an order status API and take appropriate steps based on the results. Once you’re dealing with autonomous systems that can actually set things in motion, having a tool that shapes and controls their interactions becomes invaluable.
AI Gateways’ Role in Preventing and Mitigating Problems Stemming from Irresponsible AI Use
If left unmanaged, AI adoption at the enterprise level weakens security and obscures visibility into workflow automation processes. Getting help from top AI gateway providers doesn't automatically make all AI safe to use. However, they let organizations tailor oversight and control by monitoring AI traffic, applying security policies, and identifying potentially risky requests. The following problems become much more likely without the AI gateway layer.
Sensitive data may leave the company unnoticed
Some employees still expose sensitive information like credentials, customer insights, or legal and financial data by feeding them to external models without understanding where it goes or how it’s handled. An AI gateway can create an inspection point between applications and AI providers. This is where sensitive information may be identified and prompts that contain it rejected or flagged.
No one has complete insight into AI use
It’s not uncommon for enterprising employees to use shadow AI alongside approved tools if they think the latter are less powerful or efficient. Previously, security teams could only enact policies designed for the tools they knew about. Gateways’ oversight and monitoring capabilities let them identify all applications that make AI requests, what models they’re contacting, the nature of the requests, and the users responsible for making them.
Prompt injection risk is high
Employees can use AI responsibly and still cause prompt injection if the websites, emails, and other sources that AI tools access to process their prompts contain malicious instructions. Gateways ensure that the guardrails you put in place to combat this are in effect regardless of the AI tool or request.
Unchecked AI agents can do more harm
The worst a rogue chatbot can do is provide an incorrect response. Meanwhile, the misuse of AI agents with permissions to access databases or modify code can escalate into a serious cybersecurity incident. Gateways reduce the risk by providing various controls. These can range from straightforward authentication and rate limits to restricting potentially dangerous operations and limiting which requests certain apps or agents can make in the first place.
