Every AI feature eventually becomes a URL someone can call. The moment a model is behind an API, it inherits every classic API risk, plus a new set unique to AI: prompts that try to manipulate the model, requests designed to run up compute costs, and outputs that can leak more than intended. Protecting that endpoint is not optional hardening for later, it's part of shipping the feature at all.
This guide covers what actually threatens an AI endpoint, the core practices that address it, and how NevTan Cloud handles this layer by default for deployments and agents like Hermes Agent.
See How NevTan Cloud Secures Deployments → cloud.nevtan.com/security
Table of Contents
(For the CMS: each entry links to the matching section anchor.)
Why AI Endpoints Are a Different Kind of Attack Surface (#different-surface)
Common Threats to AI APIs and Endpoints (#common-threats)
Core Practices for Protecting AI Endpoints (#core-practices)
Protecting Endpoints Behind AI Agents (#agents-and-endpoints)
Unprotected vs. Protected: What Changes (#comparison)
How NevTan Cloud Protects Your Endpoints by Default (#how-nevtan-protects)
Compliance, Trust & Legal Commitments (#compliance-trust)
Pricing & Availability (#pricing)
Frequently Asked Questions (#faq)
Final Thoughts (#final-thoughts)
Why AI Endpoints Are a Different Kind of Attack Surface
A conventional API has a fixed, predictable input shape, a field expects a number, a string, a known enum. An AI endpoint's input is open-ended natural language, which means the same field that accepts a legitimate question can also carry an attempt to manipulate the model's behavior.
The cost model is different too. A conventional request is cheap to reject; an AI request that reaches the model has already consumed GPU time, whether or not it should have been allowed in the first place. And the output side carries its own risk: a model can be coaxed into revealing context it was given, or producing content that was never meant to leave the system.
None of this means AI endpoints are unsecurable, it means the standard API security checklist is necessary but not sufficient.
Common Threats to AI APIs and Endpoints
Prompt injection. Crafted input designed to override the system's instructions and make the model ignore its guardrails or reveal its configuration.
Data and prompt exfiltration. Attempts to extract training data, system prompts, or other users' context through carefully constructed queries.
Resource exhaustion and cost abuse. High-volume or expensive requests intended to run up compute cost or degrade service for other users.
Unauthorized access. Calls made without valid credentials, or with credentials that have broader access than the caller should have.
Malformed and adversarial input. Feeding a model malformed or adversarial input specifically to trigger errors, crashes, or unexpected behavior.
Scraping and automated abuse. Automated scripts hitting an endpoint at a rate no legitimate user would, often to scrape outputs at scale.
Core Practices for Protecting AI Endpoints
Strong authentication and scoped access. Every call should carry a verifiable identity, scoped to exactly what that caller is allowed to do, nothing implicit or shared.
Rate limiting and quotas. Caps on requests per caller stop both accidental floods and deliberate abuse before they reach the model.
Input validation and output filtering. Check input against expected shape and size before it reaches the model, and filter output before it reaches the caller.
Encryption in transit and at rest. Data in transit and at rest should be encrypted as a baseline, not a differentiator.
Logging and monitoring. Every call logged with enough detail to reconstruct what happened, so unusual patterns are visible, not just guessed at.
Anomaly detection and alerting. Alerts on unusual volume, cost, or error patterns should reach a human before the incident becomes a headline.
Regular credential rotation and review. Credentials and access scopes should be reviewed and rotated on a schedule, not left in place indefinitely.
Protecting Endpoints Behind AI Agents
An agent like Hermes Agent adds a layer on top of a raw model endpoint, it can read documents, take actions, and carry context across a conversation, which means the endpoint behind it needs the same protections as any other API, plus attention to what the agent is allowed to access and act on.
Scope what the agent can access. An agent should only reach the systems and data it needs for its task, not everything the account happens to have connected.
Treat retrieved content as untrusted. Treat any content an agent reads (a document, a webpage, a message) as untrusted input that could contain an injection attempt, the same way you'd treat user input.
Log agent actions, not just conversations. Actions an agent can take on your behalf should be logged the same way API calls are, so a review of what happened is always possible.
The reassurance here isn't that agents are risk-free, it's that the same endpoint-protection discipline that applies to a raw API applies to an agent sitting in front of one, and shouldn't be treated as a separate problem.
Unprotected vs. Protected: What Changes
Aspect | Unprotected Endpoint | Protected Endpoint |
Access control | Shared or implicit credentials | Scoped, verifiable per-caller identity |
Abuse resilience | One caller can degrade service for all | Rate limits contain abuse per caller |
Visibility into incidents | Discovered after the fact, if at all | Flagged by monitoring in near real time |
Cost exposure | Unbounded on malicious traffic | Capped by quotas and alerts |
Data exposure | Unfiltered output can leak context | Output checked before it reaches the caller |
How NevTan Cloud Protects Your Endpoints by Default
Deployments on NevTan Cloud, including model endpoints and agents like Hermes Agent, inherit these protections from the platform rather than requiring every team to build them independently, consistent with the approach described on our security and trust pages.
Authentication built into every deployment. Every deployed endpoint requires authenticated, scoped access by default, not as an optional add-on.
Rate limiting out of the box. Rate limiting and quota enforcement apply automatically, without a separate configuration project.
Monitoring included from day one. Request and response monitoring is active from the first call, giving visibility without a monitoring stack to build.
Anomaly detection at the platform level. Traffic patterns are watched for the volume and cost signals that typically indicate abuse.
This is the same principle behind the platform generally: security is inherited by using it, not assembled separately by each team that deploys on it.
Compliance, Trust & Legal Commitments
Endpoint security sits alongside a set of published legal and compliance commitments. Uptime and support for deployed endpoints are covered under the SLA, acceptable use of the platform (including automated and high-volume traffic) is governed by the Acceptable Use Policy, and the overall relationship is set out in the Terms of Service.
Data protection specifics live in the privacy policy and the AI data policy, with every third party involved in delivering the service disclosed in the subprocessor list. Browser-level data handling is covered separately in the cookie policy.
Pricing & Availability
Endpoint protections described here are included by default for deployments on NevTan Cloud, they are not a separate line item. Current plans and rates are maintained on the pricing page, useful to review when estimating cost for a production deployment at scale.
Frequently Asked Questions
What makes AI API security different from typical API security?
AI endpoints accept open-ended natural language input and can be metered by cost-intensive compute, which introduces prompt injection, data exfiltration through model output, and cost-based abuse in addition to standard API risks like unauthorized access.
Do I need to build my own rate limiting and monitoring for an AI endpoint?
Not on a managed platform. Authentication, rate limiting, and monitoring are handled by the platform by default, so individual teams do not need to build this layer themselves.
Does securing an endpoint mean my data is used to improve the model?
No. Security and data usage are separate commitments. NevTan Cloud does not use customer data to train models, as detailed in the AI data policy, regardless of the security controls in place.
How is uptime for a deployed AI endpoint guaranteed?
Through the published SLA, which sets out the platform's uptime and support commitments for deployed endpoints.
Where can I read the legal detail?
Start with the privacy policy, terms of service, Acceptable Use Policy, SLA, subprocessor list, cookie policy, and AI data policy.
Final Thoughts
An AI endpoint that isn't protected isn't really shipped, it's exposed. The good news is that most of what protects it, authentication, rate limiting, monitoring, is not a novel research problem, it's disciplined application of practices that already exist, applied to a surface that happens to accept natural language instead of structured fields. Built in from the start, none of it slows a team down; bolted on after an incident, all of it costs more than it would have the first time.
See How NevTan Cloud Secures Every Deployment → cloud.nevtan.com/security
