SOLUTIONS

Prompt Injection

Data Leakage & PII

Employee AI Use

Agentic AI Risk

Brand Protection

SOLUTIONS

Agentic AI risk

Chatbots answer questions. Agents do things: they call your APIs, move money, merge code, and send emails on your behalf. When one goes wrong, through a hijacked instruction, a misread goal, or a reasoning chain that drifts, it acts for real. That risk arrives the moment you give an AI permission to act.

Our agents can take actions we can't audit.

We follow your agent's whole chain of reasoning, not just the final action, and stop a bad step before it runs.

WHAT'S DIFFERENT

What changes when AI can act

An agent acts instead of just answering. That creates three problems you never had with a chatbot.

Multi-Step Reasoning Drift

Give an agent a goal and it makes its own plan. A hallucination or a misread instruction a few steps in, and it ends up taking an action you'd never have approved.

Expanded Attack Surface

Agents take in far more than your prompt: support tickets, database results, file attachments, tool outputs. Hidden instructions in any of it can quietly redirect what the agent does.

Blind Audit Trails

Your logs show the final action, the changed record, the sent email. They don't show the reasoning that led there, so when something goes wrong, you can't tell what convinced the agent to do it.

REAL INCIDENTS

It's already happening

As companies move from chatbots to agents, the failures change shape. These three are public, and none of them needed a traditional hack.

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A support agent talked into refunding fraud

A coding agent wiped a live database

One poisoned email spread across a network of agents

THE STAKES

The consequences don't stay technical

When an agent goes wrong, the cost is real. It shows up in your operations, your finances, and your standing with regulators.

Operational Damage

Agent incidents leave real footprints: modified records, unauthorized emails, or merged code. Cleaning up these unapproved actions takes weeks, and some of it can't be reversed.

Fraud Exposure

When agents hold permissions to move money, grant access, or issue refunds, prompt manipulation becomes a direct path to financial fraud. The more it can do, the bigger the loss.

Regulatory Exposure

"The AI did it" is not a defence. Under rules like the EU AI Act, an agent that decides on its own brings strict obligations, and you'll be expected to show exactly what it did, and why.

THE FIX

How Beyond Guard Addresses It

Agent risk isn't one problem with one fix. The threat surfaces are spread across the prompts that drive the agent, the content the agent reads, the tools the agent calls, and the connections the agent makes to external systems.

Intent & Action Governance

Agent Guard

Monitors the agent’s internal planning process and strictly limits the real-world actions it is allowed to execute.

Workflow

The user gives the agent a high-level task.

Agent Guard continuously monitors the agent's internal reasoning chain to catch plan drift or infinite loops before an action is taken.

The guard enforces strict tool whitelisting, verifying that the agent is permitted to call that specific API or parameter.

The action is mapped back to a human identity, ensuring every single transaction is fully attributed and authorized in the audit logs.

External Connection Security

MCP Guard

Discovers, risks-scores, and regulates the connections your agents make to external MCP servers.

Workflow

The agent attempts to connect to an external tool or data source via an MCP server.

MCP Guard automatically discovers the connection and scores the destination server for potential security risks.

The guard intercepts the handshake before the connection is established to scan for malicious tool descriptions designed to hijack the agent.

Unauthorized or high-risk external connections are blocked entirely, isolating the agent within your secure perimeter.

Indirect Input Sanitization

Context Guard

Strips hidden instructions out of the content your agent reads, before it acts on them.

Workflow

The agent reads external content required for its task (such as incoming emails, customer support tickets, or retrieved RAG documents).

Context Guard intercepts this data before it blends into the agent's active context window.

The fine-tuned classification engine parses the raw text to locate hidden "indirect prompt injection" payloads.

The proxy sanitizes the content, removing adversarial commands while safely passing the clean data to the agent.

RESOURCES

What we're learning and sharing.

Research reports, threat intelligence, deployment playbooks, and the occasional blunt opinion on where the AI security category is going.

See All

Research

Adversarial Multi-Turn Prompt Injections

Guide

AI Security Glossary

Research

Decoding the Latest AI Exploits

The agent attempts to connect to an external tool or data source via an MCP server.