AI Security

What is AI Security?

AI security is the discipline of protecting artificial intelligence systems — including large language models (LLMs), machine learning pipelines, and agentic AI deployments — from a growing range of adversarial threats. As AI becomes embedded in enterprise infrastructure, security teams must address risks that did not exist in traditional software environments, including model manipulation, training data poisoning, and prompt injection attacks.

Description

The AI security attack surface spans the entire AI lifecycle: data collection and training, model deployment, inference-time interactions, and integration with external tools and APIs. Attackers target AI systems to extract sensitive training data, manipulate outputs, bypass safety controls, or use the AI as a pivot point into connected enterprise systems. According to Cisco's State of AI Security 2026, most organizations that deployed agentic AI moved forward with limited readiness — creating exposure across model interfaces, tool integrations, and supply chains. The ethical implementation of AI in cybersecurity requires treating AI components as first-class security assets with defined threat models, access controls, and monitoring. Key AI security domains include adversarial machine learning (attacks that fool models into producing wrong outputs), model inversion (extracting training data from model responses), supply chain attacks targeting AI frameworks and model registries, and runtime attacks like prompt injection.

Usage and Examples

An organization deploys a customer-facing LLM chatbot integrated with its internal knowledge base and CRM. Without proper AI security controls, an attacker can craft inputs that cause the model to reveal confidential customer records, bypass content filters, or execute unauthorized actions through connected APIs. Real-world incidents in 2025 demonstrated that a single crafted email processed by Microsoft 365 Copilot could silently exfiltrate data from OneDrive, SharePoint, and Teams — illustrating that AI security failures can have the same blast radius as traditional system compromises. Organizations building AI-powered workflows should also be aware of shadow AI risks, where employees deploy unauthorized AI tools that operate outside sanctioned security controls. Learn how enterprises are finding LLMs they don't know about.

How Does This Relate to Penetration Testing?

AI security penetration testing — also called AI red teaming — evaluates the security posture of AI systems by simulating adversarial attacks against models, pipelines, and integrations. Testers probe for prompt injection vulnerabilities, model extraction risks, insecure API integrations, overprivileged tool access, and authentication weaknesses in AI agent orchestration frameworks. As organizations race to deploy AI, the gap between deployment and security readiness creates measurable risk that structured penetration testing can identify and quantify before attackers exploit it. The how to test for prompt injection guide from Evolve Security's research team provides a technical starting point for security teams beginning to assess their AI attack surface. Evolve Security offers dedicated AI Penetration Testing to help organizations identify vulnerabilities in their AI systems before adversaries do.