AI Security: The New Frontier for Enterprise Teams

AI security became a board-level conversation in 2024. Not because AI security is new — adversarial ML attacks have been documented for years — but because AI systems are now making decisions that matter: credit approvals, insurance pricing, fraud flags, hiring screens. The attack surface has expanded to the point where it's in the same risk category as application security and data security, not a research curiosity.

Here's what enterprises need to do now, not after the first incident.

The Attack Surface That's Different from Traditional Software

Traditional application security has well-understood attack patterns: injection, broken authentication, insecure deserialization. AI systems introduce new attack surfaces that traditional security tooling doesn't cover.

Prompt injection — when user-supplied text is incorporated into a prompt and that text includes instructions designed to override the model's intended behavior. An LLM-based customer support system that processes customer messages as input is vulnerable to customers inserting "ignore previous instructions" payloads. The defense is input sanitization, output validation, and architectural separation between instruction prompts and user-supplied content.

Data poisoning — when training data is manipulated to cause the model to learn incorrect associations. For models trained on user-generated content or operational data, this is a real risk. The defense is training data curation, anomaly detection in training data, and evaluation against hold-out sets that include adversarially crafted examples.

Model extraction — an attacker queries your model extensively to reconstruct it. For proprietary models, this is an IP theft risk. The defense is rate limiting on model serving endpoints, anomaly detection on usage patterns, and watermarking outputs in ways that allow attribution.

Your Take on the Model Governance Gap

The regulatory pressure is real: the EU AI Act, sector-specific AI regulations in financial services, and emerging regulatory frameworks in healthcare all require documentation of AI system behavior, testing against failure modes, and defined human oversight for high-risk decisions. Most enterprises are not ready for these requirements.

The specific gap: model documentation. The EU AI Act requires documentation of training data characteristics, known limitations, and intended use cases for high-risk AI systems. Most model registries contain a name, a version number, and some evaluation metrics. The documentation requirement is an order of magnitude more detailed. This documentation has to be written by humans who understand the system — it cannot be auto-generated — and the time to write it is before the regulatory deadline, not after a finding.

What Enterprises Must Do Now

Classify your AI systems by risk level before your regulatory environment does it for you. High-risk systems (those making consequential decisions about individuals) need the full documentation treatment: training data provenance, evaluation methodology, known limitations, intended use case scope, and defined monitoring. Start with your highest-impact production systems and work down the risk ladder.

Build security testing into your AI development lifecycle, not as a late-stage audit but as a development-phase activity. Test your prompt handling against injection patterns. Test your model outputs against adversarial inputs. Include security evaluation in your production readiness criteria. The incident response cost — regulatory, reputational, remediation — is orders of magnitude higher than the prevention cost. As always, I'm here to help.