AI Security Isn’t Broken, We’re Just Defending the Wrong Things

The cybersecurity industry has a pattern for when a new technology emerges, we immediately start building walls around it. We did it with cloud, we did it with containers, and now, we’re doing it with AI, except this time, the walls we’re building are in completely the wrong places.

Walk into any enterprise security review today, and you’ll hear the same priorities: securing AI models, protecting training data, validating outputs, and deploying AI-powered copilots. Vendors are rushing to sell “AI security” tools that focus exclusively on model-level controls, such as guardrails, prompt-injection defenses, and model-monitoring platforms.

But attackers are using your AI integrations as highways into everything else.

The Real Attack Surface Nobody’s Watching

One pattern we consistently observe across enterprise environments tells a concerning story of security teams investing heavily in securing their AI development environments: model access controls, data governance frameworks, MLOps security tooling. This gives a false confidence that their AI is “locked down.”

But when you map the actual attack surface, you see AI chatbots often hold OAuth tokens to dozens of SaaS platforms, API keys with excessive cloud permissions, and identity trust relationships that can create direct paths from a simple prompt injection to production infrastructure. The models themselves may be secure, but the ecosystems they live in are often wide open, and this isn’t an edge case.

Enterprises now use an average of 130+ SaaS applications, with AI integrations spanning identity providers, cloud infrastructure, databases, and business-critical systems. Each integration is a potential attack path, and each API connection is a trust boundary that attackers are actively probing.

The problem isn’t that our AI security tools are broken. It’s that we’re securing individual components while attackers are exploiting the connections between them.

Why Model-Centric Security Misses the Point

The current approach to AI security operates on a fundamental misunderstanding of how modern attacks work. We treat AI as a standalone asset that needs protection, similar to how we might secure a database or web application. But AI in production doesn’t exist in isolation. It’s a node in a complex graph of identities, permissions, APIs, and data flows.

Consider a typical enterprise AI deployment. You’ve got an AI agent with access to your Google Workspace. It’s connected to Salesforce through APIs. It’s integrated with Slack for notifications. It pulls data from AWS S3 buckets. It’s authenticated through Okta or Azure AD. It triggers workflows in ServiceNow.

Traditional AI security focuses on the model itself: its security posture, prompt validation, output safety. But attackers are focused on the integrations: what they can reach through compromised service accounts, where they can pivot through API manipulations, which trust boundaries they can cross through exploited integrations.

The attack doesn’t start or end with the AI model. The model is just the entry point.

Attack Paths Don’t Respect Product Boundaries

Here’s where most organizations get stuck. They’ve deployed security tools that each provide visibility into a single domain. One tool monitors cloud permissions. Another tracks SaaS configurations. A third manages identity governance. A fourth handles vulnerability management.

Each tool shows you its piece of the puzzle. None of them show you how the pieces connect.

According to Gartner, organizations now use an average of 45+ security tools. Yet despite this massive investment, attackers are successfully chaining together misconfigurations across these domains because no single tool can see the complete attack path.

An attacker doesn’t need to find a critical vulnerability in your AI model. They just need to find a chain. Maybe it’s a misconfigured IAM role attached to your AI service, which has permissions to an S3 bucket, which contains credentials to a SaaS application that has admin access to your production environment.

Each individual misconfiguration might score “medium” or “low” in your security tools. But chained together? That’s a critical exposure. And it’s completely invisible if you’re looking at each security domain in isolation.

The Exposure Management Imperative

This is why the conversation needs to shift from “AI security” to continuous threat exposure management for AI-integrated environments.

It’s not enough to ask whether our AI models are secure. Security teams need to understand what an attacker can actually reach if they compromise an AI service account. They need visibility into how misconfigurations across cloud, SaaS, and identity systems could be chained together. They need to know how AI integrations are changing their attack surface in real time. And they need to prioritize risks based on actual attackability, not just severity scores.

Most security programs still prioritize risks in isolation, using CVSS scores and compliance checklists that completely ignore whether a vulnerability is actually exploitable in your specific environment.

This gap is even more pronounced with AI systems because they change constantly. New integrations are added weekly. Permissions evolve. API connections shift. Your attack surface from last month is not your attack surface today, but your security assessment probably is.

What Attack-Path-Aware Security Actually Looks Like

Securing AI in production requires a fundamentally different approach, and it comes down to four key shifts in thinking.

First, you need unified visibility across security domains. Stop asking each security tool to operate in its own silo. Your cloud security, identity governance, SaaS management, and vulnerability scanning tools all hold pieces of the attack path puzzle. They need to share data in real-time so you can see how misconfigurations chain together.

Second, embrace continuous attack path simulation. Don’t wait for penetration tests or red team exercises to discover exploitable paths. Continuously test how an attacker could move through your environment, focusing on actual exploitability rather than relying on theoretical severity scores.

Third, prioritize based on context. A misconfigured S3 bucket isn’t critical just because it’s public. It’s critical if it’s public and contains credentials and those credentials have privileged access, and they’re reachable from an internet-exposed asset. Context matters more than any individual score.

Fourth, move toward preemptive remediation. By the time your SOC team is investigating an alert, you’ve already lost valuable response time. Modern defense requires the ability to close exploitable paths before they’re weaponized, not after an incident.

The Warning We Can’t Ignore

As AI becomes embedded across every layer of the enterprise stack, the attack surface is expanding faster than security teams can manually reason about it. We’re adding AI integrations at 10 times the pace we’re securing them.

If you’re securing AI in isolation, protecting the model while ignoring the ecosystem it operates in, you’re already behind. Attackers don’t think in tools, they think in paths. They don’t exploit individual vulnerabilities. They chain together misconfigurations across your entire environment.

The enterprises that will successfully secure AI won’t be the ones with the most AI security tools. They’ll be the ones who understand that AI security is inseparable from exposure management across their entire attack surface.

Model security is table stakes. What matters is understanding what an attacker can reach when they compromise an AI integration. Until security teams can answer that continuously, in real-time, across their entire environment, they’re not securing AI. They’re just hoping the walls they’ve built are in the right places.