Atomic Data Leadership Forum: AI on Offense and Defense in Minneapolis

October 01, 2025 Scott Evangelist

Leadership Forum Recap

On September 30 in downtown Minneapolis, Minnesota, leaders from finance, manufacturing, and technology joined Atomic Data to examine how to capture AI’s productivity upside while defending against AI-powered cyber threats. The discussion focused on practical steps that reduce risk while enabling teams to pilot, measure, and scale value.

Topics: AI Readiness · AI Governance · SOC as a Service · Shadow AI · Data Strategy

Why AI Readiness Matters Now

AI adoption is accelerating and risk exposure rises with it. Treat AI as both offense for productivity, automation, and insight, and defense to counter attackers using the same tools. Start with small, governed pilots, set metrics, and build a roadmap that links investment to outcomes.

Chris Heim, CEO, Atomic Data

“AI is a powerful offensive tool for business. But the same technology is being used against us by bad actors moving just as fast—if not faster.”

The AI-Driven Threat Landscape

What Is Changing

Generative AI scales phishing and ransomware with convincing language and timing.

Synthetic video and audio personas enable social engineering and impersonation.

Credential theft markets expand via AI-assisted discovery and automation.

Translating Risk

Exposure increases as attack sophistication meets low-barrier tools. Expect faster attack cycles and broader surface areas. Map the top business processes, data repositories, and identities that matter most, then align controls to those assets.

Wade Hoffman, Security Practice Lead

“AI takes traditional threats like phishing and amplifies them. Unsophisticated attackers now launch highly convincing, large-scale attacks.”

AI-Powered Security Operations

Defense accelerates when AI analytics and human expertise work in tandem across endpoints, networks, cloud, and SaaS. Visibility, triage discipline, and rapid containment cut dwell time and blast radius.

Detection

Continuous Anomaly Analysis

Machine learning surfaces outliers and suspicious patterns in near real time. Analysts validate, prioritize, and enrich with context to drive action.

Response

Containment With Context

24×7 analysts isolate users and devices, revoke tokens, and coordinate remediation to reduce dwell time. Playbooks turn lessons into faster future response.

Tony Pietrocola, President, AgileBlue

“The only way to stay ahead is to combine AI with people. AI catches patterns at scale, but critical thinking comes from humans.”

Read the Solution Brief

Client Path to AI Readiness

Wanner Engineering: Why a Managed SOC

Insurance alignment: cyber insurers increasingly expect managed detection and response backed by evidence.

Scale without headcount: SOC outcomes without building a 24×7 team or reassigning scarce engineers.

Trusted integration: Fit with Atomic Data services, processes, and escalation rules for faster MTTR.

Jim Blackwood, Director of IT

“We knew we needed a managed SOC. Partnering with Atomic Data was the right fit—cost-effective, integrated, and backed by people we trust.”

AI Beyond Security: Productivity and Data Strategy

Operational ROI

Automation for invoice data entry, meeting capture, and email triage creates measurable time savings and faster follow-through. Track reclaimed hours and error rates to quantify ROI.

Unified Data Foundation

Clean, consolidated data enables copilots and analytics to deliver accurate summaries, forecasts, and decisions. Invest in data quality and permissions before scaling use cases.

Dustin Saunders, Data Intelligence Practice Lead

“The biggest ROI comes from applying AI to your own data—turning complexity into actionable insight.”

See the Case Study

Governance and Shadow AI

Shadow AI occurs when employees use unapproved tools that may train on or expose sensitive data. Readiness requires policy, provisioning, and oversight with clear accountability.

Immediate Steps

Provide sanctioned enterprise AI tools and disable training on proprietary data by default.

Publish an AI acceptable-use policy and review quarterly with examples and red lines.

Stand up an AI governance committee to vet tools, vendors, and data flows.

Policy Accelerator

Use our editable template to set scope, data handling, and guardrails quickly. Align with legal, security, and HR to drive adoption.

Download AI Policy Template

Key Takeaways

Balance

Treat AI as offense and defense. Build a roadmap that funds productivity wins while closing priority risks. Sequence by impact and feasibility.

Modern SOC

Combine AI analytics with expert analysts. Focus on visibility, triage quality, and rapid containment to cut dwell time and reduce business impact.

Data First

Reliable AI outcomes require clean, unified, and governed data. Invest in quality, lineage, and role-based access before scaling copilots.

Governance

Set policy, provide approved tools, and monitor usage to curb shadow AI. Educate teams and refresh controls as models and risks evolve.

Join Us in Minneapolis · December 2

For the follow-up AI Leadership Forum – Part II with practical insights into corporate AI readiness, best-practice guardrails, and ensuring AI ROI.

Reserve Your Seat

Location: Minneapolis, Minnesota · Hosted by Atomic Data · Leadership Forum Series