Security Protocols
Standardized workflows across defense, civilian security, and justice reform.
Protocol Categories
| Category | What It Covers | Key Protocols |
|---|---|---|
| Defense | Threat detection --> autonomous response --> assessment | Drone deployment, AI targeting, cyber defense |
| Civilian Surveillance | Sensor deployment --> AI analysis --> alert --> response | Camera networks, predictive analytics, community safety |
| Justice | Detection --> assessment --> intervention --> rehabilitation | Risk scoring, electronic monitoring, AI coaching |
| Evidence | Collection --> chain of custody --> verification | Blockchain evidence, digital forensics |
Defense Protocol: Autonomous Targeting
The shift from human-driven to AI-augmented kill chains.
1. SENSE
'-> Satellite, drone, ground sensor data feeds
'-> Multi-domain awareness (land, sea, air, cyber, space)
2. CLASSIFY
'-> AI object recognition (Project Maven)
'-> Threat categorization and priority scoring
'-> Targeting from hours to minutes
3. DECIDE
'-> Human-in-the-loop for lethal decisions
'-> AI recommends, human authorizes
'-> Rules of engagement validation
4. ACT
'-> Autonomous drone deployment ($500-$3,000 per unit)
'-> Swarm coordination for saturation
'-> Electronic warfare countermeasures
5. ASSESS
'-> Battle damage assessment via AI
'-> Feedback to improve future classification
'-> After-action reporting
Ukraine Battlefield Data
| Metric | 2023 | 2025 |
|---|---|---|
| Monthly FPV production | 20,000 | 200,000 |
| AI-guided strike accuracy | Baseline | 2-4x improvement |
| Cost per engagement | ~$500 | ~$500 (10% premium for AI) |
| 2025 drone production target | -- | 4.5 million UAVs |
AI-guided FPV drones handle the critical final 500 meters where electronic warfare is most intense. A human pilot still selects the target. The machine closes the kill.
US Drone Dominance
The Pentagon is restructuring around autonomous systems.
| Program | Scale | Timeline |
|---|---|---|
| Drone Dominance Program | 200,000+ drones ordered | Delivery by 2027 |
| First delivery batch | 30,000 drones | July 2026 |
| Average unit cost | ~$3,000 | Current |
| Budget for autonomous systems | $1.8B (FY2025) | 24% YoY increase |
| Collaborative Combat Aircraft | 1,000 AI-piloted jets | $6B contract competition |
Project Maven
Started in 2017 to put AI into military intelligence. Targeting timelines dropped from hours to minutes. By June 2026, Maven will begin transmitting "100 percent machine-generated" intelligence to combatant commanders. Palantir holds the $1.3B contract through 2029.
Civilian Surveillance Protocol
AI Camera Network Deployment
1. DEPLOY
'-> Camera/sensor placement (coverage optimization)
'-> Network integration (shared data layer)
'-> AI model training on local patterns
2. MONITOR
'-> Real-time behavioral analysis
'-> Anomaly detection (not just face matching)
'-> Cross-camera tracking and correlation
3. ALERT
'-> Threat classification and confidence scoring
'-> Notification to responders with context
'-> False alarm filtering (90% reduction with AI)
4. RESPOND
'-> Dispatched response with pre-arrival intelligence
'-> Evidence capture automated
'-> Outcome logging for model improvement
5. LEARN
'-> Pattern recognition across incidents
'-> Predictive heat mapping
'-> Community feedback integration
Crime Reduction Evidence
| Intervention | Crime Reduction | Study |
|---|---|---|
| CCTV with active monitoring | 20-50% | Urban Institute |
| License plate readers (Flock) | Measurable drop in property crime | Community studies |
| Gunshot detection (ShotSpotter) | Faster response, contested prevention | Multiple cities |
| Behavioral AI (Ambient.ai) | 90% false alarm reduction | Vendor data |
Justice Protocol: Risk-Based Intervention
From Incarceration to Monitoring
1. ASSESS
'-> AI risk scoring (COMPAS, PSA, or newer tools)
'-> Multi-factor evaluation: history, behavior, support network
'-> Bias audit on scoring outputs
2. ASSIGN
'-> High risk: supervised facility with AI rehabilitation
'-> Medium risk: electronic monitoring + community program
'-> Low risk: community supervision + check-ins
3. MONITOR
'-> GPS ankle monitoring ($5-25/day vs $100-300/day prison)
'-> Behavioral pattern analysis
'-> AI-powered check-ins and coaching
4. REHABILITATE
'-> Personalized AI education (adaptive learning)
'-> AI therapy companions (Echo: 28% infractions drop, 32% voluntary participation increase)
'-> Skill matching to employment opportunities
'-> Prison communication reform (Ameelio free alternatives vs $1/min Securus)
5. REINTEGRATE
'-> Graduated freedom based on behavioral data
'-> Community support network activation
'-> Long-term monitoring with declining intensity
Cost Comparison
| Approach | Daily Cost | Annual Cost | Recidivism |
|---|---|---|---|
| Maximum security prison | $200-300 | $73,000-$110,000 | 68% (3yr) |
| Minimum security prison | $80-120 | $29,000-$44,000 | 55% (3yr) |
| Electronic monitoring | $5-25 | $1,800-$9,000 | 30-40% (estimated) |
| Nordic rehabilitation model | $350+ | $130,000+ | 18% (2yr) |
Personalized Education
AI-powered adaptive learning provides individualized education -- impossible with one teacher per 100+ prisoners.
| Benefit | Traditional | AI-Augmented |
|---|---|---|
| Curriculum personalization | One-size-fits-all | Adaptive to skill level |
| Learning pace | Class-dependent | Individual |
| Skill-to-job matching | Generic | Market-aligned |
| Availability | Scheduled classes | 24/7 access (with tablets) |
| Language support | Limited | Multilingual |
Prison Communication
The existing prison tech industry extracts rather than rehabilitates.
| Metric | Value |
|---|---|
| Securus + ViaPath market share | ~80% of US prison calls/video |
| Securus annual revenue | ~$700M |
| Tablets distributed | 600,000+ by mid-2023 |
| Revenue model shift | Phone calls to tablet services |
For-profit prison communication companies charge families $1/minute for calls. Reform organizations (Ameelio) offer free alternatives. The incentive structure works against rehabilitation.
Cyber Defense Protocol
AI-Augmented Cyber Operations
| Phase | Traditional | AI-Augmented |
|---|---|---|
| Detection | Signature-based rules | Behavioral anomaly detection |
| Analysis | Human SOC analyst | AI triage + human escalation |
| Response | Manual containment | Automated isolation + remediation |
| Recovery | Manual restoration | AI-guided system recovery |
| Learning | Post-incident report | Continuous model retraining |
Activity-to-Player Matrix
| Activity | Military | Police | Security Firms | Tech Companies | Justice System | Community |
|---|---|---|---|---|---|---|
| Drone Deployment | Primary | Supporting | ||||
| Camera Networks | Supporting | Primary | Primary | Supporting | ||
| AI Analysis | Supporting | Supporting | Primary | |||
| Risk Assessment | Primary | Primary | ||||
| Electronic Monitoring | Supporting | Primary | Supporting | Primary | ||
| Rehabilitation | Supporting | Primary | Supporting |
Context
- Performance -- How to measure protocol effectiveness
- Players -- Who executes these protocols
- Platform -- Technology enabling these workflows
- Protocols -- General protocol patterns