Defense AI Operations Officer (DAOO)™ Workshop

• – Cyber defense operations and SOC acceleration

• – ISR tasking, prioritization, and fusion

• – Battlefield awareness and operational picture enhancement

– Building an AI-enabled operational command model:

• – Decision layers (tactical / operational / strategic)

• – Authority matrix (who can approve what when AI is involved)

• – AI confidence thresholds and override rules

–  Operational governance controls for AI-in-command:

• – Logging and traceability for decisions supported by AI

• – Auditability during live operations

• – Accountability and command responsibility in AI-assisted decisions

– Multi-domain C2 integration: land, air, maritime, cyber, space, and intelligence coordination

• – AI in SOC: alert triage, correlation, anomaly detection, response orchestration

• – Threat hunting workflows powered by AI

• – AI-based deception detection and deepfake-driven incident triggers

• – Operational handling of AI false positives/false negatives in cyber battle rhythm

– ISR Operations

• – AI-supported collection management: tasking, prioritization, coverage gaps

• – Multi-source fusion (OSINT/SIGINT/IMINT/HUMINT augmentation)

• – Target recognition risk: misclassification, spoofing, adversarial manipulation

– Field / Security Operations

• – AI-supported situational awareness and operational routing

• – Use of AI for predictive threat mapping and force protection

• – AI in access control, perimeter defense, and anomaly detection

– Operational planning for AI deployment: requirements, readiness, interoperability constraints

– Contingency handling: operating when AI systems degrade, fail, or are compromised

• – Mission objectives and AI support functions

• – Data availability requirements and operational data pipelines

• – Key performance indicators (KPIs) for AI operational contribution

– Mission decision design:

• – Defining “decision checkpoints” where AI advice is allowed

• – Mandatory human approval gates for high-impact actions

• – “Abort triggers” and safety stops (when AI signals instability)

– Operational workflows:

• – Real-time operational picture update loop

• – AI-driven task allocation and resource optimization

• – Coordinated response playbooks across units/agencies

– Running operations under uncertainty:

• – Confidence calibration (when to trust AI, when to challenge it)

• – Handling conflicting AI outputs (multiple systems disagreeing)

– Cross-agency coordination using AI:

• – Defense + intelligence + cyber + internal security + legal liaison

• – Coordination models for peacetime vs crisis vs wartime

– Interoperability realities:

• – Data classification barriers and information-sharing constraints

• – Secure intelligence fusion without overexposure

– Rules of engagement integration:

• – AI usage policy alignment across agencies

• – Shared escalation protocols and approval authority

– Operational communication discipline:

• – Preventing AI-amplified misinformation inside command rooms

• – Handling deepfakes and manipulated intelligence reports

– Creating a unified operational picture without over-reliance on AI

• – Adversarial AI and model evasion

• – Data poisoning and intelligence manipulation

• – Deepfakes affecting command decisions

• – Automation bias and “AI authority effect”

– Failure modes in live operations:

• – Model drift during missions

• – Sensor spoofing and false signals

• – Overfitting to past patterns

– Escalation & stability risks:

• – AI-driven speed causing premature escalation

• – Misinterpretation leading to disproportionate responses

• – AI-induced confusion during multi-domain conflict

– Operational risk controls:

• – Pre-mission validation checklists

• – Runtime monitoring triggers

• – Fallback operating modes (“AI degraded mode”)

• – Decision logging and after-action forensic traceability

– Practical response: how to continue mission safely when AI becomes unreliable

• – Operational objectives and mission phases

• – AI-enabled C2 structure and decision authority matrix

• – AI system roles across cyber/ISR/field operations

• – Escalation gates, override rules, and contingency plans

• – Operational risk register and mitigation actions

– Deliver an Operational Command Briefing and defend:

• – Why AI is used at specific points

• – Where humans must retain decision authority

• – How failures and adversarial manipulation will be handled

• – Mixed-domain incidents (cyber + ISR + field operations)

• – Conflicting intelligence signals and time pressure

• – Deepfake-driven misinformation injects

• – AI system degradation and adversarial manipulation events

– Multi-agency crisis coordination drill:

• – Joint operational decisions and escalation control

• – Resource allocation under constraints

• – Final operational resolution and stabilization plan

– After-action review (AAR):

• – What AI improved, what it distorted, what must change

• – Operational lessons learned and playbook refinement

• – Executive briefing for operational readiness improvement