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AI Security Guide

AI doesn't behave like anything security teams are used to.

It sprawls without approval, leaks data through outputs, and takes actions no one explicitly authorized.

We've put together a guide to the 4 AI security risks most prevalent in enterprise environments right now — showing what each one looks like in practice and how to start closing the gaps.

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The Threat Landscape

4 AI risks closing in on enterprise security

Each risk below is active in real environments today. Understanding what they look like in practice is the first step to closing the gaps.

RISK 01

Shadow AI & Unsanctioned Tool Sprawl

What it is

Employees adopt AI tools without IT approval — writing assistants, code generators, image tools, browser plugins. Each one becomes a potential data exfiltration point. Unlike shadow IT of the past, AI tools actively process and transmit sensitive content.

In practice

"A sales rep pastes a client contract into an AI summarizer to prep for a call. The tool stores that data, trains on it, and the organization has no visibility, no consent record, and no way to retrieve it."

Common gaps

  • No approved AI tool catalog
  • No browser extension policy
  • No data classification enforcement at the endpoint
RISK 02

Data Leakage Through AI Outputs

What it is

AI models — including internal deployments — can surface data they were never meant to expose. Prompt injection, model inversion, and membership inference attacks allow adversaries (or curious insiders) to extract training data, PII, or proprietary information through carefully crafted queries.

In practice

"An internal AI assistant trained on HR documents is queried by a manager with a cleverly worded prompt. The model returns salary ranges, performance notes, and termination details for employees the manager has no HR access to."

Common gaps

  • No output filtering or DLP on AI responses
  • Overly broad training data ingestion
  • No role-based access controls on AI knowledge bases
RISK 03

Unauthorized AI-Driven Actions

What it is

Agentic AI systems — those that can browse the web, execute code, send emails, or call APIs — take actions on behalf of users. Without strict guardrails, these agents can be manipulated into performing actions no human explicitly approved, from sending emails to modifying files to making API calls.

In practice

"A customer service AI agent with email access is tricked via a prompt injection in an inbound message. The attacker's payload instructs the agent to forward all incoming emails to an external address — and it complies, because no action boundary was defined."

Common gaps

  • No action scope limits on AI agents
  • No human-in-the-loop approval for sensitive operations
  • No audit logging of AI-initiated actions
RISK 04

Identity & Access Exploitation via AI

What it is

AI dramatically lowers the cost of social engineering. Deepfake voice and video, hyper-personalized phishing, and AI-generated pretexting make traditional identity verification unreliable. Attackers use AI to bypass MFA, impersonate executives, and craft attacks that evade legacy email security filters.

In practice

"A finance team member receives a video call from what appears to be the CFO, authorizing a wire transfer. The video is a deepfake. The voice is cloned from public earnings call recordings. The request bypasses every existing approval control because it appears to come from a trusted identity."

Common gaps

  • No out-of-band verification protocol for financial requests
  • Legacy email filters not tuned for AI-generated content
  • No deepfake detection in video conferencing workflows
Ready to close the gaps?

Know your exposure.
Before attackers do.

Boni-Fi's cybersecurity team assesses your AI tool footprint, access controls, and data handling policies — then builds a remediation roadmap tailored to your environment.

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