Enterprise security teams face a new reality: AI concentrates risk and multiplies attack surfaces faster than policies can keep up.
Hot take: AI in the enterprise didn’t just add another tool — it rewired the threat model. Coverage of Deloitte’s Enterprise AI findings highlights that AI has moved from experiments into core workflows, concentrating sensitive data and creating new failure modes across data, models, apps, and infra[3].
What happened: Deloitte surveyed orgs and found agentic AI pilots, model poisoning risks, plugin/API attack vectors, and AI-driven automation reshaping escalation paths — all of which demand a new security playbook rather than tweaks to legacy controls[3]. For developers and platform engineers this means rethinking how you handle credential scope, model access, provenance, and observability from day one, not as an afterthought[3].
Practical implications / opinion: Build detection and red-team automation that tests your ML pipeline end-to-end, adopt adversarial testing for models, and treat models as first-class assets in your CMDB. The old perimeter mindset is obsolete; defenders must use AI at the same velocity as attackers to keep up[3].
Question to you: Which piece of your stack would break first under an AI-driven attack — the data layer, the model, or the plugin surface?