AI-Assisted Data Exfiltration Planning

Supports planning of covert data exfiltration by minimizing transfer volume and adapting theft methods to network and security constraints.

The Problem

“Defensive AI for Detecting and Disrupting Data Exfiltration Planning”

Organizations face these key challenges:

1

Static DLP rules miss novel or fragmented exfiltration techniques

2

Analysts struggle to connect weak signals across tools and time windows

3

Large alert volumes create triage fatigue and delayed response

4

Encrypted traffic and sanctioned SaaS channels obscure intent

Impact When Solved

Detects low-and-slow exfiltration preparation before large transfers occurCorrelates endpoint, network, identity, and content signals into a single risk viewReduces analyst triage time with AI-generated incident summaries and recommended actionsImproves protection of source code, customer data, and proprietary documents

The Shift

Before AI~85% Manual

Human Does

  • •Review DLP, SIEM, proxy, and endpoint alerts for possible exfiltration activity
  • •Manually correlate user, asset, timing, and transfer clues across separate investigations
  • •Hunt for staging behaviors such as archive creation, removable media use, and off-hours access
  • •Decide containment actions and escalate incidents after confirming suspicious transfer patterns

Automation

  • •Apply static detection rules to flag known bulk transfer or policy violation events
  • •Generate basic alert correlations from predefined thresholds and signatures
  • •Surface logs and historical events for analyst search and case review
With AI~75% Automated

Human Does

  • •Approve high-impact containment actions such as host isolation, session revocation, or token invalidation
  • •Decide how to handle high-risk cases, business exceptions, and sanctioned data movement needs
  • •Review AI-prioritized incidents and confirm whether behavior reflects likely exfiltration planning

AI Handles

  • •Continuously monitor endpoint, identity, network, and content signals for coordinated exfiltration planning behaviors
  • •Correlate fragmented events into risk-scored cases and generate concise incident summaries with recommended actions
  • •Retrieve relevant playbooks and prior incident patterns to assess staging, covert channel selection, and policy evasion
  • •Simulate likely next exfiltration paths under observed constraints and execute low-risk blocking actions within policy

Operating Intelligence

How it works

AI surfaces what is hidden in the data.

Humans do the substantive investigation.

Closed cases sharpen future detection.

Confidence93%
ArchetypeDetect & Investigate
Shape6-step funnel
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapefunnel

Step 1

Scan

Step 2

Detect

Step 3

Assemble Evidence

Step 4

Investigate

Step 5

Act

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI scans and assembles evidence autonomously. Humans do the substantive investigation. Closed cases improve future scanning.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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