Cybersecurity AI security · Agents · Governance

Adopt AI agents — without the new risk .

AI agents don't just answer; they act — calling tools, touching data, making changes. That power is the risk. Billow's AI and agent security and governance services put guardrails around your LLMs and agents — prompt injection defense, least-privilege access, monitoring and the governance to adopt AI responsibly.

  • LLM & AI agent security
  • Prompt injection & jailbreak defense
  • Agent guardrails & least privilege
  • Responsible AI governance

Overview

What is AI and agent security?

AI security protects AI systems — LLMs, AI agents, and the data and tools they use — from misuse, manipulation and failure; AI agent security specifically protects autonomous agents that don't just answer questions but take actions on your behalf. As AI moves from chatbots to agents with real access, security has to move with it.

 

The shift that changes everything is autonomy. A chatbot that says the wrong thing is embarrassing; an agent that does the wrong thing — with access to your data, systems and tools — is a breach. And AI introduces attacks traditional security never faced: prompt injection, where hidden instructions in a document or web page hijack the agent; jailbreaks that bypass its guardrails; and data leakage through the model itself. A single crafted input can turn a helpful agent into an insider threat.

 

Billow helps you adopt AI without inheriting that risk. We secure your LLMs and agents with input and output guardrails, prompt injection defense, least-privilege access to tools and data (including MCP connections), and monitoring of what agents actually do — wrapped in an AI governance framework so adoption is responsible, compliant and accountable.

What's included

Guardrails from model to action

Our AI and agent security services cover the whole risk surface — the model, its inputs and outputs, the tools and data it touches, and the governance around it all.

Prompt injection & jailbreak defense

Input filtering, isolation and output validation to stop hidden instructions from hijacking your AI's behavior.

Agent guardrails & least privilege

Constrain what agents can do — scoped tool access, approval gates and least-privilege permissions on every action.

MCP & tool-access security

Secure the connections agents use to reach tools and data — authenticated, scoped and monitored, not wide open.

AI monitoring & observability

Log and monitor what agents actually do — prompts, tool calls and actions — so misuse and anomalies are caught.

Data protection for AI

Stop sensitive data leaking into or out of models — controlling what AI can access and what it can reveal.

AI governance & risk framework

Policies, risk assessment and oversight — aligned to frameworks like the NIST AI RMF — so AI adoption is accountable.

Why AI & agent security

An agent with access is an insider — secure it like one.

The moment AI can act — not just talk — it inherits the risk of any privileged account, plus new attacks all its own. Securing it isn't optional; it's the price of adopting AI at all.

Defend the new attacks

Prompt injection and jailbreaks are attacks traditional security never faced. We defend against them specifically.

Least privilege for agents

An agent should only reach what it needs. Scoped access and approval gates keep a compromised agent contained.

See what AI does

Monitoring every prompt, tool call and action means misuse is visible — not a black box you have to trust blindly.

Adopt with confidence

An AI governance framework lets you say yes to AI responsibly — with the controls and accountability to prove it.

Frameworks & approach

Built on recognized AI security standards

We align to the emerging standards for AI risk and security — so your program is credible, not improvised.

OWASP

OWASP LLM Top 10

LLM & agent risks

We assess and defend against the OWASP Top 10 for LLMs — prompt injection, data leakage, insecure tool use and more.
NIST

NIST AI RMF

AI risk management

Governance grounded in the NIST AI Risk Management Framework — structured, defensible AI risk and oversight.
ZT

Zero Trust for agents

Least privilege & verification

Zero Trust principles applied to AI — every agent action verified, scoped and least-privilege by default.

How we engage

From assessment to managed cloud

01

Assess

We map your AI systems, agents and their access, and identify the risks — prompt injection, over-permission, data exposure.

02

Secure

We add guardrails, injection defense, least-privilege tool access and monitoring around your LLMs and agents.

03

Govern

We put the AI governance framework in place — policies, risk assessment and oversight aligned to recognized standards.

04

Monitor

Ongoing monitoring of agent behavior and new AI risks keeps your controls current as models and threats evolve.

FAQ

AI security questions

AI security is the practice of protecting AI systems — large language models (LLMs), AI agents and the data and tools they use — from misuse, manipulation and failure. It addresses risks unique to AI, like prompt injection, data leakage through models, and agents taking harmful actions, on top of the traditional security of the infrastructure they run on.
AI agent security protects autonomous AI agents — systems that don't just answer questions but take actions, call tools and access data on your behalf. It's different because an agent can be manipulated into doing something harmful with real access, so it needs guardrails, least-privilege permissions, human oversight and monitoring — securing what the agent can do, not just what it can say.
Prompt injection is an attack where malicious instructions are hidden in the input an AI processes — a web page, document or email — to hijack its behavior. For an agent with access to tools and data, a successful prompt injection can turn it against you: exfiltrating data or taking unauthorized actions. Defending against it is a core part of AI agent security.
AI governance is the framework of policies, controls and oversight that ensures AI is used responsibly, safely and in compliance with regulation. It covers what AI systems are allowed to do, how their risks are assessed and monitored, how data is handled, and who is accountable — so you can adopt AI with confidence rather than exposure.
MCP (Model Context Protocol) is a standard that lets AI agents connect to tools and data sources. It's powerful but expands the attack surface — each connection is access an agent can use or be manipulated into misusing. We secure MCP with least-privilege scoping, authentication, monitoring and guardrails around what agents can reach and do.

Related services

Where AI security connects

Securing AI draws on identity, data protection and the AI systems themselves. These relate most closely.

Cybersecurity

The full security practice this sits within — endpoint, SIEM & SOAR, cloud security, information protection and more.

AI Agents & MCP

The agents we help you build — secured from the start, with the guardrails and governance baked in.

Identity & Access Management

Agents need identities and least-privilege access too — IAM is the backbone of controlling what AI can reach.

Get started

Adopting AI? Secure it first.

Before an agent touches your data and tools, it needs guardrails. Tell us what you're building or running, and we'll help you deploy AI securely and govern it responsibly.