This year’s Black Hat had a clear undercurrent: the real battle is in knowing your data and AI… and keeping it in check.
Data governance is cool again due to AI: it’s all about the data so CISOs are increasingly taking ownership of it, but they want governance with controls. The inventory of AI assets is recognized as a core need because you can’t protect what you don’t know. Visibility into the prompt for data and exploits remains top of mind consistently over the years, but now we see more granular identification of use cases between the protection of home grown applications vs. employee access to AI applications, which is triggering questions of how to intercept and protect the different threat vectors with minimal complexity or agent proliferation.
Below are the top takeaways that dominated discussions among CISOs, security architects, and risk leaders.
1. AI Security and Governance Are Front and Center
As generative AI becomes embedded across business functions, security leaders are being asked to account for everything: the models, the data, the access, the risk. The conversation has moved beyond theoretical threats. Teams want real answers to real questions: Where are our models? What sensitive data are they using? Who’s prompting them, and what can they do?
Security leaders are prioritizing:
- Full inventory of AI models and datasets, especially those trained on or using sensitive data
- Repeatable risk assessments that evaluate AI usage, model behavior, and regulatory exposure
- Controls that govern how data flows into AI systems, with classification and lineage visibility
- Prompt-level access control and policy enforcement to prevent misuse or leakage
This isn’t about securing AI in the future. It’s about securing the AI that’s already in production today.
2. Data Visibility Is Still the Bottleneck
Despite years of investment, most organizations still struggle to answer fundamental questions about their data. What do we have, where is it, and who can access it? Without a clear picture of their data landscape, security teams can’t protect what matters or prove compliance.
The most common themes included:
- Scalable, automated data discovery across cloud, on-prem, and hybrid environments
- Classification that adapts to business-specific context, not just generic patterns
- Deep visibility into cloud platforms like Snowflake and S3, where data sprawl is accelerating
- Lineage tools that connect data discovery to configuration management and policy enforcement
Before you can control risk, you need control over your data. That starts with knowing what you have.
3. Security Needs to Integrate Into the Way the Business Works
Security tools that create new silos get pushed aside. Security tools that fit into existing workflows get adopted. The message was consistent: if your platform can’t plug into the rest of the ecosystem, it’s going to be shelfware.
Security teams are actively looking for:
- Tight integrations with GRC platforms, IAM, DLP, UEBA, and existing data pipelines
- Connections into ServiceNow, Snowflake, and other business-critical systems
- Ways to operationalize security findings through existing processes, not new ones
The goal isn’t just detection. It’s alignment, action, and accountability across the organization.
4. Risk Scoring Isn’t Enough Without Action
Risk registers and scoring frameworks are only useful if they lead to something. Boards want clarity. GRC teams want consistency. Security wants the ability to act.
Leaders are raising the bar:
- Risk scores need to be mapped to real-world threats, frameworks, and obligations
- Reporting must be digestible by non-technical audiences and tied to business impact
- Remediation options should be embedded into the platform, not left to manual effort
If the output of risk analysis is a number and a dashboard, you’re only halfway there. Security teams need risk tools that help them do something about it.
5. Lightweight, Agentless Deployment Is the New Standard
Security budgets are tightening. Resources are stretched. And complexity is the enemy. CISOs are no longer willing to take on tools that require agents, proxies, or prolonged implementation timelines.
The preference is clear:
- Agentless deployments that reduce operational overhead
- API-first architectures and browser-based tools that integrate without disruption
- Minimal lift for security engineering and operations teams
The less effort it takes to deploy and maintain a tool, the faster it becomes useful—and the more likely it is to stay in use.
BigID Announcements at Black Hat 2025
BigID made a bold showing at Black Hat with a series of major product announcements that reflect where the industry is headed—focusing squarely on AI governance, sensitive data protection, and real-time risk visibility. The highlights:
- AI-Powered Prompt Classification Engine: the industry’s first natural language interface for data discovery and classification. With this launch, BigID is replacing rigid rule-based systems with an AI-powered engine that lets users describe what they’re looking for in plain English, then automatically finds and classifies the data.
- Shadow AI Discovery: Uncover unauthorized or rogue AI models across the enterprise. This feature provides security teams with full visibility into hidden AI deployments and ungoverned model usage.
- AI Data Labeling for Usage Enforcement: Enforce AI-specific usage policies to ensure that sensitive or regulated data is only used appropriately by AI models and applications.
- AI Data Cleansing: Get AI data readiness, including intelligent cleansing of sensitive and regulated data – and enable organizations to reduce risk before data ever enters an AI pipeline.
- Watchtower for AI & Data: It’s situational awareness for your AI and data risk. BigID delivers ongoing alerts and contextual insights into model behavior, data usage, and policy violations across the enterprise, at a glance.
- AI TRiSM (Trust, Risk, and Security Management): Manage the trust, risk, and security of AI models and data – from AI SPM to risk assessments and beyond.
Together, these innovations showcase a shift from passive reporting to proactive control over AI systems and the sensitive data they consume—helping enterprises move faster, with more confidence, in a high-stakes AI landscape.
Final Word: Security Is Evolving – So Must the Tools
Security leaders don’t want more noise, more dashboards, or more theoretical frameworks. They want clarity. Control. Confidence. The future of cybersecurity lies in visibility, integration, and action – especially as AI becomes more embedded in how businesses operate.
The takeaways from Black Hat 2025 make it clear: modern security teams demand visibility, flexibility, and actionability. The players in this next era will be platforms that understand how to operationalize insights, integrate into existing ecosystems, and secure AI from the inside out.
Want to stay ahead of the curve? Prioritize solutions that understand the shift… and build for it. See how BigID can help your organization connect the dots in data and AI.
