Agentic AI Security: New Risks and Controls in the Databricks AI Security Framework (DASF v3.0)

The emergence of agentic AI security frameworks signals a maturation moment for data platforms. As someone who's built pipelines in Databricks, I can tell you that autonomous agents accessing your lakehouse introduce attack surfaces we haven't traditionally defended. Unlike batch ETL jobs with predictable data flows, agents make dynamic decisions about which tables to query and how to interpret results—requiring security controls that operate at runtime rather than deployment time. This matters because it forces us to rethink governance layers. We can't just apply role-based access control at the warehouse level anymore; we need observability into agent decision-making, data lineage for agent outputs, and probably additional validation layers before agents write back to canonical tables. The broader trend here is that platforms are converging toward unified AI/data stacks, which means security can't be an afterthought bolted on later. My recommendation: if you're considering agentic workflows in your lakehouse, inventory your most sensitive datasets now and map which ones agents legitimately need. Then build explicit guardrails—perhaps using tools like PII masking or query validators—before agents touch production data. This proactive stance beats reactive incident response.