🔮 EU Parliament Monitor — Future Threat Model

🛡️ Evolving Threat Landscape & Planned Security Controls
🔍 Future Architecture Threats • AI/LLM Security • Advanced Democratic Protection

📋 Document Owner: CEO | 📄 Version: 2.0 | 📅 Last Updated: 2026-03-19 (UTC)
🔄 Review Cycle: Quarterly | ⏰ Next Review: 2026-06-19
🏷️ Classification: Public (Open Civic Transparency Platform)

📚 Architecture Documentation Map

Category	Document	Description	Status
🏛️ Architecture	ARCHITECTURE.md	C4 model system architecture	✅ Current
📊 Data Model	DATA_MODEL.md	Entity relationships and data flow	✅ Current
🔄 Flowchart	FLOWCHART.md	Process workflows and data flows	✅ Current
📈 State Diagram	STATEDIAGRAM.md	System state transitions	✅ Current
🧠 Mind Map	MINDMAP.md	Conceptual system relationships	✅ Current
💼 SWOT	SWOT.md	Strategic analysis	✅ Current
🛡️ Security	SECURITY_ARCHITECTURE.md	Security controls and architecture	✅ Current
🎯 Threats	THREAT_MODEL.md	Current threat landscape (20 threats)	✅ Current
🔮 Future Threats	FUTURE_THREAT_MODEL.md	This document — Future threat analysis	📋 Planning
🚀 Future Architecture	FUTURE_ARCHITECTURE.md	Architectural evolution roadmap	📋 Planning
🚀 Future Security	FUTURE_SECURITY_ARCHITECTURE.md	Planned security enhancements	📋 Planning

🎯 Purpose & Scope

This document identifies emerging threats and planned security controls for the EU Parliament Monitor as it evolves from a static site generator into an advanced European Parliament intelligence platform. It complements the current THREAT_MODEL.md with forward-looking analysis of threats that will materialize as new capabilities are added.

🌟 Transparency Commitment

As an open-source European Parliament monitoring platform, this future threat model is published publicly to:

🔍 Demonstrate Proactive Security: Show commitment to anticipating threats before they materialize
📋 Enable Community Review: Allow security researchers to review planned defenses
🏛️ Democratic Accountability: Ensure transparency in protecting democratic information systems
🤝 Build Trust: Provide evidence of systematic security planning to stakeholders

📚 Framework Integration

This future threat model follows the Hack23 ISMS Threat Modeling Policy framework:

STRIDE Framework: Threat categorization per future system component
MITRE ATT&CK: Technique mapping for emerging attack vectors
ENISA Threat Landscape: EU-specific threat intelligence integration
OWASP LLM Top 10: AI/LLM-specific threat classification
CIA Triad: Confidentiality, Integrity, Availability impact analysis

🔗 Reference Documents

Document	Purpose
THREAT_MODEL.md	Current threat landscape (20 threats, v2.0)
FUTURE_ARCHITECTURE.md	Planned architectural evolution
FUTURE_SECURITY_ARCHITECTURE.md	Planned security controls
Hack23 ISMS - Threat Modeling	Policy framework
Hack23 ISMS - Secure Development	Secure SDLC requirements
Hack23 ISMS - Vulnerability Management	Vulnerability lifecycle management

🔄 Planned Architecture Evolution

📊 Architecture Transition Timeline

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timeline
    title EU Parliament Monitor Architecture Evolution
    section Current (2026 H1)
        Static Site Generator : Node.js + EP MCP Server
        GitHub Pages : CDN-delivered static HTML
        14 Languages : Template-based generation
    section Phase 2 (2026 H2)
        AI Content Pipeline : LLM-enhanced news generation
        Confidence Scoring : Automated fact-checking
        Enhanced Monitoring : Real-time EP data validation
    section Phase 3 (2027)
        Real-Time Dashboard : Dynamic parliamentary analytics
        API Gateway : Public API for EP data access
        Community Features : User feedback and reporting
    section Phase 4 (2028+)
        Predictive Analytics : AI-powered political forecasting
        Multi-Parliament : Extended to national parliaments
        Federation : Decentralized transparency network

📊 Attack Surface Evolution

flowchart LR
    subgraph "Current Attack Surface"
        direction TB
        C1[📄 Static HTML] --- C2[🔌 EP MCP Client]
        C2 --- C3[⚙️ GitHub Actions]
        C3 --- C4[📦 npm Dependencies]
    end

    subgraph "Phase 2 Attack Surface"
        direction TB
        P2_1[🤖 LLM Pipeline] --- P2_2[📊 Confidence Scoring]
        P2_2 --- P2_3[🔍 Fact Checking]
        P2_3 --- P2_4[📡 Enhanced Monitoring]
    end

    subgraph "Phase 3 Attack Surface"
        direction TB
        P3_1[🌐 API Gateway] --- P3_2[👥 User Accounts]
        P3_2 --- P3_3[💬 Community Features]
        P3_3 --- P3_4[📊 Real-Time Dashboard]
    end

    subgraph "Phase 4 Attack Surface"
        direction TB
        P4_1[🌍 Multi-Parliament] --- P4_2[🔗 Federation Protocol]
        P4_2 --- P4_3[🧠 Predictive AI]
        P4_3 --- P4_4[📈 Analytics Engine]
    end

    C4 -.->|"+8-12 threats"| P2_1
    P2_4 -.->|"+10-15 threats"| P3_1
    P3_4 -.->|"+5-8 threats"| P4_1

    style C1 fill:#e8f5e9
    style P2_1 fill:#fff4e1
    style P3_1 fill:#ffe1e1
    style P4_1 fill:#f3e5f5

💎 Future Critical Assets & Protection Goals

🏗️ Asset-Centric Analysis for Future Architecture

Asset	Phase	CIA Classification	Protection Priority
AI/LLM Models	Phase 2	C:Low, I:Critical, A:High	Model integrity, provenance verification
API Keys & OAuth Tokens	Phase 3	C:High, I:High, A:Medium	Secret management, rotation policies
User Account Data	Phase 3	C:High (GDPR), I:High, A:Medium	Privacy by design, encryption at rest
Community Content	Phase 3	C:Public, I:High, A:Medium	Content integrity, moderation
Federation Credentials	Phase 4	C:Critical, I:Critical, A:High	Mutual TLS, certificate management
Predictive Models	Phase 4	C:Medium, I:Critical, A:Medium	Model integrity, bias prevention
Cross-Parliament Data	Phase 4	C:Low, I:Critical, A:High	Data reconciliation, source verification

🔐 Crown Jewel Analysis (Future State)

Crown Jewel	Threat Category	Worst-Case Impact	Protection Strategy
Democratic Content Integrity	Data Manipulation	Public misinformation from trusted source	Multi-layer validation, confidence scoring, human review
User Privacy (GDPR)	Data Breach	Regulatory fines, reputation damage	Privacy by design, data minimization, encryption
AI Model Integrity	Model Poisoning	Systematically biased political content	Model provenance, training data audit, bias detection
Federation Trust	Protocol Abuse	Cross-platform trust compromise	Mutual TLS, zero-trust architecture, audit logging

🆕 Future Threat Categories

🤖 FT-001: AI/LLM Content Generation Threats

Applies to: Phase 2 (AI Content Pipeline)

Threat	Description	STRIDE	MITRE ATT&CK	Likelihood	Impact	Mitigation Strategy
LLM Prompt Injection	Adversarial EP data crafted to manipulate LLM output during news generation	Tampering	T1059	Medium	High	Input sanitization, prompt engineering guardrails, output validation
LLM Hallucination	AI generates plausible but incorrect parliamentary information	Tampering	N/A	High	High	Confidence scoring, human-in-the-loop for <0.85 confidence, cross-reference validation
Model Poisoning	Training data manipulation to bias AI-generated content	Tampering	T1565	Low	Critical	Model provenance verification, training data audit, bias detection
LLM Data Leakage	AI model inadvertently exposing sensitive information in generated content	Information Disclosure	T1530	Low	Medium	Output filtering, PII detection, content review pipeline
Adversarial Prompt via EP Data	Crafted parliamentary text exploiting LLM instruction-following	Tampering	T1059.006	Medium	High	Input boundary enforcement, system prompt hardening
Model Supply Chain Attack	Compromised LLM model weights or framework dependency	Tampering	T1195	Low	Critical	Model checksums, signed artifacts, provenance verification

OWASP LLM Top 10 Alignment:

OWASP LLM ID	Threat	EU Parliament Monitor Relevance	Planned Control
LLM01	Prompt Injection	EP data used as LLM input could contain injection vectors	Input sanitization, prompt hardening
LLM02	Insecure Output Handling	Generated HTML could contain unsafe content from LLM	Output validation, CSP, auto-escaping
LLM04	Model Denial of Service	Excessive EP data could overwhelm LLM processing	Rate limiting, input size caps, timeout enforcement
LLM05	Supply Chain Vulnerabilities	LLM model or framework dependencies could be compromised	Model provenance, dependency scanning
LLM06	Sensitive Information Disclosure	LLM might include sensitive patterns from training data	Output filtering, content review
LLM09	Overreliance	Trusting LLM output without verification	Confidence scoring, human review queue

🌐 FT-002: API Gateway & Dynamic Content Threats

Applies to: Phase 3 (Real-Time Dashboard)

Threat	Description	STRIDE	MITRE ATT&CK	Likelihood	Impact	Mitigation Strategy
API Abuse	Rate limiting bypass, credential stuffing on API endpoints	Denial of Service	T1110	Medium	Medium	OAuth2/API keys, rate limiting, WAF rules
Server-Side Request Forgery	API gateway exploited to access internal resources	Elevation of Privilege	T1190	Low	High	Strict allowlisting, network segmentation
Real-Time Data Poisoning	Malicious data injected into live dashboard feeds	Tampering	T1565	Low	High	Schema validation, anomaly detection, data signing
Session Hijacking	Authenticated user sessions compromised	Spoofing	T1539	Low	Medium	Secure session management, HTTPS-only, SameSite cookies
GraphQL Injection	Malicious queries exploiting GraphQL endpoint complexity	Tampering	T1190	Medium	Medium	Query depth limiting, complexity analysis, rate limiting
WebSocket Hijacking	Real-time data stream interception or manipulation	Spoofing	T1557	Low	High	WSS (TLS), origin validation, message authentication

👥 FT-003: Community Feature Threats

Applies to: Phase 3 (Community Features)

Threat	Description	STRIDE	MITRE ATT&CK	Likelihood	Impact	Mitigation Strategy
User-Generated Content Abuse	Spam, disinformation, or political manipulation via feedback system	Tampering	T1491	High	Medium	Content moderation, anti-spam filters, reporting mechanism
GDPR Data Breach	User personal data exposure from community features	Information Disclosure	T1530	Low	Critical	Privacy by design, data minimization, encryption at rest
Account Takeover	Community user accounts compromised for manipulation	Spoofing	T1078	Medium	Medium	MFA, rate limiting, anomaly detection
Coordinated Inauthentic Behavior	Bot networks manipulating community sentiment	Repudiation	T1583	Medium	High	Bot detection, behavioral analysis, rate limiting
Cross-Site Scripting (Stored)	User-submitted content containing XSS payloads	Tampering	T1189	Medium	High	Input sanitization, CSP, output encoding

🌍 FT-004: Multi-Parliament Federation Threats

Applies to: Phase 4 (Multi-Parliament)

Threat	Description	STRIDE	MITRE ATT&CK	Likelihood	Impact	Mitigation Strategy
Cross-Parliament Data Integrity	Inconsistent data between EU and national parliament sources	Tampering	T1565	Medium	Medium	Data reconciliation, source verification, integrity checksums
Federation Protocol Abuse	Exploiting inter-system communication for unauthorized data access	Elevation of Privilege	T1071	Low	High	Mutual TLS, API authentication, protocol validation
Jurisdiction Conflict	Different privacy laws (GDPR vs. national) creating compliance gaps	N/A	N/A	Medium	Medium	Legal review per jurisdiction, data classification, consent management
Supply Chain via Federation Partner	Compromised national parliament data source injecting malicious data	Tampering	T1195.002	Low	Critical	Source validation, data integrity checks, anomaly detection
DNS Hijacking of Federation Endpoints	Redirecting federation traffic to attacker-controlled servers	Spoofing	T1584.002	Low	High	Certificate pinning, DNSSEC, mutual TLS

🎖️ MITRE ATT&CK Future Coverage Analysis

📊 ATT&CK Tactics for Emerging Attack Surface

Tactic	Current Coverage	Phase 2 (AI)	Phase 3 (API)	Phase 4 (Federation)
Initial Access	✅ Supply chain, dependency	🔮 Model poisoning	🔮 API exploitation, credential stuffing	🔮 Federation endpoint abuse
Execution	✅ GitHub Actions	🔮 Prompt injection, LLM manipulation	🔮 GraphQL injection	🔮 Cross-parliament code execution
Persistence	✅ Repository compromise	🔮 Backdoored models	🔮 Account persistence, session fixation	🔮 Federation trust abuse
Privilege Escalation	✅ Token scope abuse	🔮 Model authority escalation	🔮 OAuth scope escalation	🔮 Cross-jurisdiction privilege
Defense Evasion	✅ SHA pinning bypass	🔮 Adversarial input evasion	🔮 WAF bypass	🔮 Cross-border evasion
Credential Access	✅ Secret exposure	🔮 API key extraction from LLM	🔮 OAuth token theft	🔮 mTLS certificate theft
Collection	✅ EP data access	🔮 Training data extraction	🔮 User data scraping	🔮 Cross-parliament data harvest
Impact	✅ Content manipulation	🔮 Systematic bias injection	🔮 Service disruption, data manipulation	🔮 Democratic process manipulation

🌳 Future Attack Trees

graph TD
    ROOT[🎯 Compromise Democratic<br/>Content Integrity] --> AI[🤖 AI Pipeline Attack]
    ROOT --> API[🌐 API/Dashboard Attack]
    ROOT --> FED[🌍 Federation Attack]
    ROOT --> SOCIAL[👥 Social Engineering]

    AI --> AI1[Prompt Injection<br/>via EP Data]
    AI --> AI2[Model Poisoning<br/>via Training Data]
    AI --> AI3[Hallucination<br/>Exploitation]

    API --> API1[GraphQL<br/>Injection]
    API --> API2[Real-Time Data<br/>Poisoning]
    API --> API3[Session<br/>Hijacking]

    FED --> FED1[Federation<br/>Protocol Abuse]
    FED --> FED2[Compromised<br/>Partner Data]
    FED --> FED3[Jurisdiction<br/>Exploitation]

    SOCIAL --> SOC1[Coordinated<br/>Inauthentic Behavior]
    SOCIAL --> SOC2[Insider<br/>Threat]

    style ROOT fill:#ff6b6b,color:#fff
    style AI fill:#fff4e1
    style API fill:#e1f5ff
    style FED fill:#f3e5f5
    style SOCIAL fill:#ffe1e1

👥 Future Threat Agent Evolution

📊 Evolving Threat Actor Landscape

Agent Type	Current Risk	Phase 2 Risk	Phase 3 Risk	Phase 4 Risk	Evolution Driver
🏛️ Nation-State Actors	Medium	High	High	Critical	AI manipulation tools, geopolitical interest in EU data
💰 Cybercriminals	Low	Medium	High	High	API monetization creates financial targets
🎭 Hacktivists	Medium	Medium	High	High	Community features enable social manipulation
👤 Malicious Insiders	Low	Medium	Medium	High	Expanded team, federation partners
🔧 Accidental Insiders	Medium	High	High	High	AI complexity increases error probability
🤖 AI-Powered Attackers	Low	High	High	Critical	Automated adversarial content generation

🎯 Future Threat Agent Capabilities

Capability	2026 (Current)	2027 (Phase 3)	2028+ (Phase 4)
Adversarial ML	Emerging	Mainstream	Advanced
Automated Content Manipulation	Basic	Sophisticated	AI-native
Cross-Platform Attacks	Limited	Moderate	Advanced (federation)
Supply Chain Sophistication	Known patterns	AI model supply chain	Federation supply chain
Democratic Process Targeting	Election periods	Continuous influence	Systemic manipulation

📊 Future Risk Assessment

🎯 Risk Matrix for Future Threats

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quadrantChart
    title 🔮 Future Threat Risk Assessment
    x-axis Low Likelihood --> High Likelihood
    y-axis Low Impact --> High Impact
    quadrant-1 Monitor & Prepare
    quadrant-2 Immediate Planning Required
    quadrant-3 Accept Risk
    quadrant-4 Design Controls Now

    "🤖 LLM Hallucination": [0.75, 0.70]
    "🤖 Prompt Injection": [0.55, 0.65]
    "🤖 Model Poisoning": [0.30, 0.85]
    "🌐 API Abuse": [0.60, 0.50]
    "🌐 SSRF": [0.35, 0.70]
    "👥 Content Abuse": [0.70, 0.45]
    "👥 GDPR Breach": [0.30, 0.80]
    "🌍 Data Integrity": [0.55, 0.55]
    "🌍 Federation Abuse": [0.30, 0.65]

📈 Quantitative Risk Scoring (Future Threats)

Threat ID	Threat	Likelihood (1-5)	Impact (1-5)	Risk Score	Priority
FT-001a	LLM Prompt Injection	3	4	12	🔴 High
FT-001b	LLM Hallucination	4	4	16	🔴 Critical
FT-001c	Model Poisoning	2	5	10	🔴 High
FT-001d	LLM Data Leakage	2	3	6	🟡 Medium
FT-001e	Adversarial Prompt	3	4	12	🔴 High
FT-001f	Model Supply Chain	2	5	10	🔴 High
FT-002a	API Abuse	3	3	9	🟡 Medium
FT-002b	SSRF	2	4	8	🟡 Medium
FT-002c	Data Poisoning	2	4	8	🟡 Medium
FT-002d	Session Hijacking	2	3	6	🟡 Medium
FT-003a	Content Abuse	4	3	12	🔴 High
FT-003b	GDPR Breach	2	5	10	🔴 High
FT-003c	Account Takeover	3	3	9	🟡 Medium
FT-004a	Cross-Parliament Integrity	3	3	9	🟡 Medium
FT-004b	Federation Protocol Abuse	2	4	8	🟡 Medium

📈 Threat Evolution Timeline

Phase	New Attack Surface	Threat Count Increase	Key New Controls Required
Current	Static site + EP MCP	20 threats (baseline)	Schema validation, CSP, SAST
Phase 2	+ AI/LLM pipeline	+8-12 threats (LLM-specific)	Confidence scoring, output validation, prompt hardening
Phase 3	+ API gateway, users	+10-15 threats (API/user)	WAF, OAuth2, rate limiting, session management
Phase 4	+ Multi-parliament federation	+5-8 threats (federation)	Mutual TLS, data reconciliation, jurisdiction management

🎯 Scenario-Centric Future Threat Analysis

🎭 Misuse Cases

Misuse Case 1: AI-Powered Disinformation Campaign

Scenario: A nation-state actor identifies that EU Parliament Monitor uses LLM-generated content. They craft adversarial European Parliament documents designed to trigger specific LLM outputs, injecting subtle political bias into generated news articles across all 14 languages.

Attack Path:

Attacker submits amendments to EP documents with adversarial text patterns
EP MCP Server fetches legitimate EP data containing adversarial content
LLM processes the data and generates subtly biased news articles
Biased content published across 14 languages, amplifying disinformation

Impact: Medium-High — Undermines democratic transparency platform credibility

Mitigation: Confidence scoring, cross-reference validation, multi-source fact-checking, human review queue for political content

Misuse Case 2: Community Feature Manipulation

Scenario: A coordinated group creates fake user accounts to systematically upvote/downvote community assessments of MEP activities, creating artificial consensus around political positions.

Attack Path:

Attacker registers multiple accounts using disposable email services
Bot network systematically rates/reviews MEP activities
Artificial consensus distorts public perception via platform

Impact: High — Platform becomes tool for political manipulation rather than transparency

Mitigation: Bot detection, behavioral analysis, rate limiting per account, proof-of-work for registration, anomaly detection on voting patterns

🤔 What-If Analysis

What-If Scenario	Probability	Impact	Response Strategy
What if EP Open Data API introduces authentication?	Medium	High	Implement OAuth2 client, update MCP server, credential rotation
What if a major LLM provider has a security breach?	Low	Critical	Model isolation, fallback to template-based generation, incident response
What if EU AI Act mandates content labeling?	High	Medium	Implement AI content disclosure, transparency watermarking
What if a federation partner is compromised?	Low	High	Mutual TLS revocation, data quarantine, partner isolation
What if coordinated attack targets during EU elections?	Medium	Critical	Election security protocols, enhanced monitoring, manual override

🛡️ Planned Security Controls

Phase 2: AI Content Pipeline Security

Control	Purpose	Priority	Timeline	STRIDE Mitigation
Confidence Scoring System	Score 0.0-1.0 for each generated article; human review if <0.85	P1	Q3 2026	Tampering
LLM Output Validation	Automated fact-checking against official EP data sources	P1	Q3 2026	Tampering
Prompt Injection Detection	Input sanitization for EP data before LLM processing	P1	Q3 2026	Tampering
Content Integrity Pipeline	Cross-reference generated content with source EP data	P2	Q4 2026	Tampering, Repudiation
AI Bias Detection	Automated political neutrality checking across 14 languages	P2	Q4 2026	Tampering
Model Provenance Verification	Signed model artifacts, checksum validation	P1	Q3 2026	Tampering

Phase 3: API & Community Security

Control	Purpose	Priority	Timeline	STRIDE Mitigation
API Gateway with WAF	Rate limiting, authentication, request validation	P1	Q1 2027	DoS, Tampering
OAuth2/OIDC Authentication	Secure user authentication for community features	P1	Q1 2027	Spoofing
Content Moderation System	Anti-spam, disinformation detection, reporting	P1	Q2 2027	Tampering, Repudiation
GDPR Compliance Layer	Privacy by design, data minimization, consent management	P1	Q1 2027	Information Disclosure
Real-Time Anomaly Detection	Monitor live data feeds for integrity violations	P2	Q2 2027	Tampering
GraphQL Query Complexity Limiting	Prevent denial-of-service via complex queries	P1	Q1 2027	DoS

Phase 4: Federation Security

Control	Purpose	Priority	Timeline	STRIDE Mitigation
Mutual TLS for Federation	Secure inter-parliament communication	P1	2028	Spoofing, Tampering
Data Reconciliation Engine	Cross-validate data between parliament sources	P1	2028	Tampering
Jurisdiction Compliance Engine	Automated GDPR/national law compliance checking	P2	2028	Information Disclosure
Zero-Trust Federation Architecture	Never trust, always verify partner data	P1	2028	Spoofing, Elevation of Privilege
Federation Audit Trail	Immutable logging of all cross-parliament operations	P1	2028	Repudiation

📋 Future Compliance Framework Mapping

📊 Emerging Regulatory Landscape

Regulation	Effective Date	Impact on EP Monitor	Required Controls
EU AI Act	2026-2027	AI content generation transparency requirements	AI content labeling, risk assessment, bias detection
EU Cyber Resilience Act (CRA)	2027	Software security requirements for open-source	SBOM, vulnerability disclosure, security updates
NIS2 Directive	Already effective	Critical infrastructure security (if classified)	Incident reporting, risk management, supply chain security
GDPR	Already effective	User data protection for community features	Privacy by design, DPO, DPIA, consent management
EU Data Act	2025-2026	Data sharing and interoperability requirements	Data portability, fair access, interoperability standards

🏛️ Future ISO 27001:2022 Control Mapping

Control	Phase 2 Relevance	Phase 3 Relevance	Phase 4 Relevance
A.5.23 Cloud Security	LLM API security	API gateway cloud deployment	Federation cloud architecture
A.8.9 Configuration Management	AI pipeline config	API & user config	Federation config management
A.8.12 Data Leakage Prevention	LLM output filtering	User data protection	Cross-border data controls
A.8.25 Secure Development	AI pipeline testing	API security testing	Federation protocol testing
A.8.28 Secure Coding	Prompt engineering	API input validation	Protocol implementation

🔄 Continuous Threat Landscape Monitoring

📡 Emerging Threat Indicators

The following developments should trigger a threat model update:

Indicator	Trigger Action	Review Priority
New LLM vulnerability class discovered	Update OWASP LLM Top 10 alignment	🔴 High
EP API major version change	Re-assess data integrity controls	🔴 High
European Parliament election period	Activate election security protocols	🔴 High
New ENISA Threat Landscape published	Update ENISA alignment section	🟡 Medium
GitHub Actions security advisory	Review CI/CD security controls	🟡 Medium
New EU regulation (AI Act, CRA update)	Update compliance mapping	🟡 Medium
National parliament data source added	Expand threat model scope	🟡 Medium
Major LLM provider breach or incident	Review AI pipeline security controls	🔴 High
Federation partner security incident	Activate partner isolation protocols	🔴 High

📅 Future Assessment Lifecycle

Assessment Type	Frequency	Trigger	Scope
Quarterly Review	Every 3 months	Scheduled	Full threat landscape review
Phase Transition Assessment	Per architecture phase	Phase completion	New attack surface analysis
Incident-Driven Assessment	As needed	Security incident	Affected threat categories
Regulatory Update Assessment	As needed	New regulation	Compliance impact analysis
ENISA-Triggered Review	Annually	ENISA report publication	EU threat landscape alignment

🎯 Future Threat Modeling Maturity

📈 Planned Maturity Progression

Level	Phase	Capabilities	Evidence
🟢 Level 2: Repeatable	Current	Structured STRIDE analysis, MITRE ATT&CK mapping	THREAT_MODEL.md v2.0
🟡 Level 3: Defined	Phase 2	AI-specific threat modeling, automated threat detection	OWASP LLM integration, CI/CD security gates
🟠 Level 4: Managed	Phase 3	Quantitative risk assessment, threat intelligence feeds	Real-time monitoring, SIEM integration
🔴 Level 5: Optimized	Phase 4	Predictive threat analysis, automated response	AI-driven threat detection, self-healing controls

Document	Description	Link
THREAT_MODEL.md	Current threat landscape (20 threats)	THREAT_MODEL.md
SECURITY_ARCHITECTURE.md	Current security controls	SECURITY_ARCHITECTURE.md
FUTURE_SECURITY_ARCHITECTURE.md	Planned security enhancements	FUTURE_SECURITY_ARCHITECTURE.md
FUTURE_ARCHITECTURE.md	Planned architectural evolution	FUTURE_ARCHITECTURE.md
Hack23 ISMS - Threat Modeling	Policy framework	Threat_Modeling.md
Hack23 ISMS - Secure Development	Secure SDLC requirements	Secure_Development_Policy.md
Hack23 ISMS - Vulnerability Management	Vulnerability lifecycle	Vulnerability_Management.md
Hack23 ISMS - Classification	Data classification framework	CLASSIFICATION.md

Approval and Review

Role	Name	Date	Signature
Security Architect	Security Team	2026-03-19	Approved
Product Owner	Product Team	2026-03-19	Approved
CEO / CISO	CEO	2026-03-19	Approved

📝 Change Log

Version	Date	Author	Changes
1.0	2026-02-26	Security Team	Initial future threat model document
			- AI/LLM threat analysis (OWASP LLM Top 10 alignment)
			- API gateway and dynamic content threats
			- Community feature threat analysis
			- Multi-parliament federation threats
			- Planned security controls roadmap (Phase 2-4)
			- Emerging threat indicator monitoring plan
2.0	2026-03-19	Security Team	Major expansion per ISMS Threat Modeling Policy
			- Added architecture documentation map
			- Added STRIDE categorization to all threats
			- Added MITRE ATT&CK future coverage analysis
			- Added future attack trees with Mermaid diagrams
			- Added threat agent evolution analysis
			- Added quantitative risk scoring
			- Added scenario-centric analysis (misuse cases)
			- Added what-if analysis
			- Added future compliance framework mapping
			- Added crown jewel analysis for future state
			- Added threat modeling maturity progression
			- Added attack surface evolution diagram

📋 Document Control:
✅ Approved by: James Pether Sörling, CEO - Hack23 AB
📤 Distribution: Public
🏷️ Classification:

This future threat model anticipates the evolving threat landscape for the EU Parliament Monitor as it advances from a static site generator to a comprehensive European Parliament intelligence platform. It demonstrates Hack23 AB's commitment to proactive security through forward-looking threat analysis aligned with the Hack23 ISMS Threat Modeling Policy.