Days 1–2: Scope and Inventory
Confirm categories, risk appetite, and artifacts to collect; assign owners
- Risk scope document
- Owner assignment matrix
- Artifact checklist
technology-strategy15 min read
Technology Risk Assessment for Investment Decisions
An investor-grade, structured approach to identify, score, and mitigate technology risks before and during funding. Includes a practical risk framework and categories, a scoring model with thresholds, a risk register template, a two-week assessment plan, governance gates, AI-specific risks, vendor risk evaluation, and an investor-ready deliverables pack.
By Technology Strategy Team
Summary
Use this structured approach to make technology risks quantifiable and investable. Define categories, score risks by likelihood and business impact, capture evidence in a risk register, and propose mitigations with decision gates and owners. Address AI, security/compliance, scalability, delivery, and vendor risks explicitly. Produce an investor-ready risk posture statement with a 30/60/90-day plan.
Why Technology Risk Assessment Matters
| Risk Gap | Business Impact | Risk Level | Financial Impact |
|---|---|---|---|
| Unidentified security risks | Data breaches, compliance failures, reputational damage | High | $500K-$2M in incident costs and fines |
| Poor scalability assessment | Growth limitations, performance degradation, customer churn | High | $400K-$1.6M in lost revenue opportunities |
| Inadequate AI governance | Quality issues, regulatory exposure, cost overruns | Medium | $300K-$1.2M in remediation and compliance costs |
| Vendor dependency risks | Service disruptions, contract disputes, exit challenges | Medium | $200K-$800K in operational disruptions |
| Weak delivery processes | Project delays, quality issues, missed milestones | High | $250K-$1M in delayed time-to-market |
| No risk quantification | Investor skepticism, valuation discounts, deal delays | High | $350K-$1.4M in valuation impact |
Technology Risk Assessment Framework
| Framework Component | Key Elements | Implementation Focus | Success Measures |
|---|---|---|---|
| Risk Categories | Security, compliance, scalability, reliability, architecture, delivery, AI, vendor | Comprehensive coverage, clear definitions | Category completeness, stakeholder alignment |
| Scoring Model | Likelihood, impact, detectability, severity calculation | Objective scoring, consistent application | Scoring consistency, decision quality |
| Risk Register | Standardized fields, evidence links, ownership, tracking | Clear accountability, evidence-based | Register completeness, update cadence |
| Assessment Process | Time-boxed approach, evidence collection, analysis | Efficient execution, thorough coverage | Timeline adherence, evidence quality |
| Governance & Gates | Decision thresholds, reporting cadence, escalation paths | Clear decision-making, timely actions | Governance effectiveness, decision velocity |
| Mitigation Planning | Action plans, owners, deadlines, progress tracking | Effective risk reduction, measurable progress | Risk reduction, plan execution |
Success Metrics and KPIs
| Metric Category | Key Metrics | Target Goals | Measurement Frequency |
|---|---|---|---|
| Risk Coverage | Categories covered, risks identified, evidence quality | 100% coverage, high evidence quality | Per assessment |
| Risk Severity | High/medium/low distribution, severity trends | Reduced high-severity risks | Monthly |
| Mitigation Effectiveness | Risk closure rate, mitigation progress, timeline adherence | >80% closure rate, on-time mitigation | Weekly |
| Governance Efficiency | Decision cycle time, gate adherence, escalation effectiveness | <7 day decision cycles | Monthly |
| Investor Confidence | Diligence outcomes, valuation impact, deal velocity | Positive diligence, minimal valuation impact | Per funding round |
| AI Risk Management | AI risks identified, governance compliance, cost control | Comprehensive AI risk coverage | Quarterly |
Risk Framework and Categories
| Category | Key Questions | Risk Level | Evidence Requirements |
|---|---|---|---|
| Security | Are controls effective and automated? | High | SBOM, SAST/DAST results, access model, rotation logs |
| Compliance & Data | Is PII governed and auditable? | High | PII inventory, policies, DSAR tests, lineage maps |
| Scalability & Performance | Can we meet growth targets cost-effectively? | High | Load test reports, capacity model, cost/unit metrics |
| Reliability & Operability | Are SLAs met with fast recovery? | High | SLO dashboards, incident retros, rollback runbooks |
| Architecture & Tech Debt | Is the system evolvable? | Medium | Architecture diagrams, runtime matrix, upgrade plan |
| Delivery & Process | Is shipping safe and predictable? | Medium | DORA metrics, CI policies, test coverage map |
| AI Governance | Is AI used responsibly and safely? | Medium | Model inventory, eval results, red-team notes, logs |
| Vendor & OSS | Is third-party risk managed? | Medium | Vendor list, SLA/SLOs, OSS health, forks/mirrors |
Team Requirements and Roles
| Role | Time Commitment | Key Responsibilities | Critical Decisions |
|---|---|---|---|
| Risk Assessment Lead | 60-80% | Overall assessment coordination, methodology, reporting | Assessment scope, risk prioritization, methodology approval |
| Security Lead | 50-70% | Security risk evaluation, compliance assessment, control validation | Security controls, compliance requirements, risk acceptance |
| Architecture Lead | 40-60% | Technical risk assessment, scalability analysis, debt evaluation | Architecture decisions, scalability plans, tech debt priorities |
| AI Governance Lead | 30-50% | AI risk assessment, model evaluation, governance compliance | AI safety standards, model selection, governance framework |
| Delivery Lead | 40-60% | Process risk assessment, delivery metrics, quality evaluation | Process improvements, quality standards, delivery priorities |
| Vendor Manager | 30-50% | Vendor risk assessment, contract review, dependency management | Vendor selection, contract terms, dependency mitigation |
Cost Analysis and Budget Planning
| Cost Category | Basic Assessment ($) | Standard Assessment ($$) | Comprehensive Assessment ($$$) |
|---|---|---|---|
| Team Resources | $30K-$70K | $70K-$175K | $175K-$420K |
| Security Tools | $15K-$35K | $35K-$85K | $85K-$200K |
| External Audits | $25K-$60K | $60K-$150K | $150K-$360K |
| AI Governance Tools | $20K-$50K | $50K-$120K | $120K-$300K |
| Consulting Services | $18K-$45K | $45K-$110K | $110K-$270K |
| Remediation Budget | $40K-$100K | $100K-$250K | $250K-$600K |
| Total Budget Range | $148K-$360K | $360K-$890K | $890K-$2.15M |
Two-Week Risk Assessment Plan
Time-boxed, evidence-first assessment
Days 3–6: Evidence Collection
Run scans, extract metrics, review policies/runbooks, and analyze logs/traces
- Evidence bundle compiled
- Initial risk register
- Gap analysis report
Days 7–9: Scoring and Options
Score risks; propose mitigations with gates; estimate cost/time; define kill-switches
- Scored risk register
- Mitigation options analysis
- Cost estimates
Days 10–12: Governance and Readout
Define decision thresholds, gates, and reporting cadence; prepare investor summary
- Governance framework
- Investor summary deck
- Reporting cadence
Days 13–14: 30/60/90 Plan
Finalize owners, milestones, and measurable outcomes with progress checkpoints
- Mitigation roadmap
- Accountability matrix
- Progress tracking system
Risk Scoring Model
| Dimension | Score 1 | Score 3 | Score 5 | Weight |
|---|---|---|---|---|
| Likelihood | Unlikely (≤ once/3 years) | Possible (annual) | Frequent (monthly/ongoing) | 40% |
| Impact | Minimal, reversible, low cost | Service degradation; contained cost | Revenue/regulatory impact; brand damage | 40% |
| Detectability | Immediate, automated detection | Detected via dashboards within hours | Hard to detect; user-reported | 20% |
| Severity Range | Risk Level | Required Action | Escalation Path |
|---|---|---|---|
| ≥ 16 | High | Immediate mitigation; investor update required | Board/Executive |
| 9–15 | Medium | Mitigate within 30–90 days; progress reporting | Management Team |
| ≤ 8 | Low | Monitor; document rationale and triggers | Team Level |
Risk Register Template (Fields)
| Field | Description | Required | Example |
|---|---|---|---|
| Risk ID | Stable identifier | Yes | SEC-001 |
| Category | From framework | Yes | Security |
| Description | Short, specific statement | Yes | Secrets found in repo X |
| Signals/Evidence | Observable facts and artifacts | Yes | Scanner output, commit hash, rotation logs |
| Likelihood (1–5) | Probability score | Yes | 4 |
| Impact (1–5) | Business effect score | Yes | 5 |
| Severity | Calculated score | Yes | 20 |
| Mitigation Plan | Concrete next steps | Yes | Rotate, add secret scanning pre-merge, audit |
| Owner | Accountable person | Yes | Security Lead |
| Due Date | Deadline for mitigation | Yes | 2024-12-01 |
| Status | Current state | Yes | Mitigating |
Risk Management Framework
| Risk Category | Likelihood | Impact | Mitigation Strategy | Owner |
|---|---|---|---|---|
| Security Vulnerabilities | High | High | Regular scanning, patch management, access controls | Security Lead |
| Scalability Limitations | Medium | High | Load testing, capacity planning, architecture review | Architecture Lead |
| AI Governance Gaps | Medium | Medium | Evaluation suites, monitoring, human oversight | AI Governance Lead |
| Vendor Dependency | Medium | Medium | Multi-vendor strategy, contract review, exit planning | Vendor Manager |
| Delivery Process Issues | High | Medium | Process improvement, metrics tracking, training | Delivery Lead |
| Compliance Failures | Low | High | Regular audits, policy enforcement, training | Security Lead |
AI-Specific Risks and Controls
Data Exposure
Prohibit production PII in external models; use secure gateways or private models
- Data protection
- Compliance assurance
- Risk reduction
Model Quality & Safety
Maintain evaluation suites, red-teaming, and drift monitoring
- Quality assurance
- Safety compliance
- Performance stability
Usage Governance
Log prompts/responses; human-in-the-loop for risky actions
- Audit trail
- Risk mitigation
- Compliance evidence
Third-Party Risk
Vendor terms, data retention, regionality, and incident SLAs reviewed
- Vendor management
- Contract compliance
- Service reliability
Cost Control
Token/unit-economics budgets with alerts and throttles
- Cost predictability
- Budget adherence
- Efficiency
Regulatory Compliance
AI-specific regulations, bias detection, transparency requirements
- Legal compliance
- Ethical AI
- Brand protection
Common Red Flags and Anti-Patterns
Unsupported Runtimes
No upgrade plan or rollback capability for end-of-life technologies
- Future-proofing
- Security compliance
- Maintainability
Missing SLOs
No service level objectives for critical services with trending incident patterns
- Operational excellence
- Reliability
- Customer trust
Security Hygiene Gaps
Secrets in code, broad production access without MFA/SSO
- Security compliance
- Access control
- Risk reduction
Capacity Unknowns
No load testing or capacity model; unknown performance under target load
- Scalability assurance
- Performance reliability
- Growth readiness
Data Governance Gaps
Undocumented PII flows, untested DSAR processes, missing retention policies
- Privacy compliance
- Data protection
- Regulatory readiness
AI Governance Absence
AI features without evaluation, usage logging, or abuse monitoring
- AI safety
- Quality assurance
- Risk management
Prerequisites
- Defined risk appetite and categories; owner assigned for each category
- Access to observability dashboards, CI/CD, scanner outputs, and policy repositories
- Service inventory, data flow diagrams, and vendor list with SLAs
- Agreement on reporting cadence and decision gates during fundraising
References & Sources
- NIST SP 800-30: Guide for Conducting Risk Assessments— Comprehensive framework for information security risk assessments
- NIST Secure Software Development Framework (SSDF) SP 800-218— Framework for secure software development practices and risk management
- ISO/IEC 27005: Information security risk management— International standard for information security risk management
- NIST AI Risk Management Framework (AI RMF) 1.0— Comprehensive framework for managing AI risks and ensuring responsible AI usage
- OWASP Application Security Verification Standard— Framework for application security controls and verification standards
- SOC 2 Trust Services Criteria— Framework for security, availability, processing integrity, confidentiality, and privacy
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Get a two-week risk assessment, a scored register with owners, and a 30/60/90 mitigation plan—plus governance gates and investor-ready artifacts.