Risk Management Strategies for Software Development Projects

Introduction

Software projects fail for many reasons, but one factor consistently separates successful projects from failures: risk management. Statistics show that projects with systematic risk management practices complete on time and on budget 30-50% more frequently than projects treating risk management as an afterthought. Yet despite this clear evidence, many development organizations continue to treat risk management as administrative overhead—a compliance checkbox rather than a strategic necessity.

Risk management is fundamentally about reducing uncertainty and increasing predictability. It's about asking difficult questions early—"What could go wrong?" "How likely is it?" "What would be the impact?" "What can we do about it?"—and acting on the answers. Organizations that master risk management don't eliminate surprises; that's impossible in complex software development. Instead, they anticipate probable challenges, prepare responses, and adapt when the unexpected inevitably occurs.

This comprehensive guide explores systematic approaches to risk management in software development. By understanding risk categories, assessment techniques, mitigation strategies, and monitoring practices, technical leaders can build organizational capabilities that transform risk from a project killer into a manageable dimension of software delivery.

Understanding Risk in Software Development

Risk is fundamentally the possibility of an undesirable outcome. In software development, risks represent threats to project objectives—schedule, budget, quality, or scope. Effective risk management recognizes that uncertainty is inherent in software development and that risk is not something to eliminate but to understand and actively manage.

Risk vs. Issue: Risk is a potential future event that hasn't occurred yet; issue is a current problem requiring immediate attention. Managing risk prevents many issues from ever occurring. When risks are unmanaged, they inevitably become issues consuming scarce project resources during crisis response.

Why Software Projects Are Uniquely Risky

Software development faces unique challenges compared to other industries. Physical engineering projects involve fewer unknowns—the laws of physics are well understood. Software development, by contrast, involves:

Knowledge Uncertainty: Teams must understand ill-defined business requirements, translate them into specifications, design systems to implement specifications, and build systems to realize designs. Each translation layer introduces interpretation risks.

Technical Uncertainty: Teams often must choose among competing technologies, architectural approaches, and implementation strategies. The "best" choice depends on factors difficult to predict until implementation progresses.

Human Dependency: Software development is knowledge work depending heavily on skilled people. Team composition, individual expertise, and interpersonal dynamics dramatically affect outcomes. These human elements are notoriously difficult to predict and control.

Change Inevitability: Requirements change as stakeholders better understand needs. Technologies evolve as platforms mature. Market conditions shift as competitive landscapes evolve. Change is not an exception; it's the normal state.

Understanding these unique characteristics helps explain why software risk management differs from risk management in other domains and why reactive approaches typically fail.

Risk Categories in Software Development

Comprehensive risk management requires understanding different categories of risks. Different categories manifest differently, require different mitigation strategies, and demand different monitoring approaches.

Technical Risks

Technical risks threaten the feasibility, performance, or quality of technical solutions.

Architectural Risks: Fundamental design decisions may prove inadequate as systems scale or requirements evolve. Selecting an architecture optimized for early performance may create bottlenecks preventing future scaling. Examples include choosing monolithic architectures later requiring painful migration to microservices, or over-engineering complexity for simple problems.

Technology Stack Risks: Selecting frameworks, languages, or databases involves commitment to specific technology ecosystems. If selected technologies become deprecated, support is abandoned, or vulnerabilities are discovered, projects face difficult choices between accepting risk or performing expensive migrations.

Integration Risks: Systems rarely operate in isolation. Integrating with external systems, legacy applications, or third-party services introduces complexity. Integration points are notorious for hidden complexity, unexpected data format mismatches, and latency issues.

Quality and Performance Risks: Code quality issues, insufficient testing, performance bottlenecks, or scalability limitations may only manifest in production under real-world load. By then, fixes are expensive and urgent.

Security Risks: Vulnerabilities in application code, underlying dependencies, or infrastructure configurations may expose sensitive data or enable unauthorized access. Security becomes increasingly expensive to address the later it's discovered.

Organizational Risks

Organizational risks relate to team composition, management, and organizational dynamics.

Staffing and Skills Risks: Unavailability of required expertise, team member departures, or insufficient training create capability gaps. When key team members leave mid-project, knowledge loss can be catastrophic.

Communication Risks: Inadequate communication among team members, stakeholders, or managers leads to misaligned expectations, duplicated effort, and conflicting decisions. Large distributed teams face particular communication challenges.

Process Risks: Inadequate project management, poor planning, unclear responsibilities, or ineffective processes create confusion and inefficiency. Lack of governance enabling ad-hoc decision-making causes problems to compound.

Estimation Risks: Unrealistic schedule estimates, optimistic time allocations, or failure to account for integration testing create schedule pressure later in projects. Estimation errors compound across the project, eventually creating crises.

Schedule and Budget Risks

Schedule and budget risks specifically threaten project timelines and financial targets.

Schedule Overruns: Inaccurate task estimation, underestimated complexity, resource unavailability, or requirement changes extend project timelines. Schedule slips often cascade—delayed tasks delay dependent tasks, creating exponential delays.

Budget Overruns: Cost estimation errors, unexpected resource requirements, or scope creep cause projects to exceed budget. Budget crises create pressure to cut corners, which typically degrades quality and introduces technical debt.

Scope Creep: Continuous addition of features or requirements beyond initial scope expands project timelines and consumes budget without corresponding value delivery. Uncontrolled scope changes are among the most common causes of project failure.

External and Business Risks

External risks originate outside the project and often outside organizational control.

Market and Business Risks: Changes in market demand, competitive landscape, or business strategy may render completed projects obsolete. Business pivots partway through development create specification changes rendering already-completed work irrelevant.

Regulatory and Compliance Risks: New regulations or compliance requirements may necessitate late-stage changes. Regulatory changes affecting data privacy, security, or industry-specific requirements can disrupt project plans.

Vendor and Third-Party Risks: Dependency on external vendors for infrastructure, services, or components creates risk when vendors fail, raise prices, or discontinue services. Supply chain disruption in software frequently stems from third-party service failures.

Risk Identification: Surfacing the Unknowns

Risk management begins with surfacing potential risks before they occur. Comprehensive risk identification creates shared awareness of threats and opportunities for proactive mitigation.

Systematic Risk Identification Approaches

Brainstorming Sessions: Gather diverse perspectives—developers, architects, testers, project managers, stakeholders—in focused brainstorming sessions asking "What could go wrong?" Diverse perspectives surface risks that homogeneous groups miss. Document all identified risks without filtering prematurely.

Historical Analysis: Review past projects examining what risks actually occurred, how they manifested, and how teams responded. Organizations maintaining risk repositories enable learning from historical precedent. Common risks affecting current projects often affected past projects as well.

Checklist Review: Create organization-specific risk checklists capturing risks commonly encountered in similar projects. Checklists ensure systematic coverage; teams reviewing checklists typically identify more risks than teams relying solely on brainstorming.

Expert Interviews: Consult subject matter experts, architects, and experienced team members about potential risks specific to projects. Experts often recognize subtle risks less experienced team members overlook.

Stakeholder Consultation: Engage business stakeholders, end-users, operations teams, and other stakeholders in risk identification. Different stakeholder groups worry about different risks—operations teams focus on operational risks while business stakeholders focus on business risks.

Process Review: Examine planned processes, methodologies, and approaches identifying procedural risks. Will selected processes work well for team size, experience level, and project complexity? Are there process gaps enabling problems?

Creating and Maintaining Risk Registers

Risk registers document identified risks in structured formats enabling tracking and management. Effective risk registers include:

• Risk ID: Unique identifier enabling consistent reference
• Risk Description: Clear description of what could go wrong
• Category: Classification (technical, organizational, schedule, budget, external)
• Probability: Likelihood of occurrence (low, medium, high, or percentage)
• Impact: Potential consequence if risk occurs (low, medium, high, or financial/schedule impact)
• Owner: Individual responsible for monitoring and responding to risk
• Mitigation Strategy: Planned response to the risk
• Status: Current status (active, mitigated, closed, materialized)
• Notes: Additional information or observations

Risk registers should be living documents, reviewed regularly and updated as circumstances change. Risks should be added as new ones emerge, and closed when they no longer pose threats.

Risk Assessment: Measuring Severity

Once risks are identified, they must be assessed to understand probability and impact, enabling prioritization of management effort.

Qualitative Risk Assessment

Qualitative assessment uses relative scales rather than numerical precision, assessing probability and impact using scales like low/medium/high.

Probability Assessment: Estimate likelihood of risk occurrence based on:

• Historical frequency in similar projects
• Current project conditions
• Team expertise and experience
• External market conditions
• Technology maturity

Impact Assessment: Estimate consequence if risk occurs:

• Financial impact (budget overrun)
• Schedule impact (delay days or weeks)
• Quality impact (defect increase)
• Scope impact (features unimplemented)
• Reputational impact (customer satisfaction)

Probability-Impact Matrix

The probability-impact matrix plots risks on two-dimensional grid, combining probability (vertical axis) and impact (horizontal axis). Risks falling in high-probability/high-impact quadrants require immediate attention; risks in low-probability/low-impact quadrants merit monitoring but not intensive management.

Typical matrix categories:

Quantitative Risk Assessment

Quantitative assessment uses numerical techniques for more precise analysis:

Expected Monetary Value (EMV): Multiplies probability by financial impact. A risk with 30% probability and $100,000 potential impact has EMV of$30,000. Organizations can sum EMVs across risks to determine total risk exposure.

Monte Carlo Simulation: Runs thousands of iterations, each assigning random values to uncertain variables. Results reveal probability distributions of outcomes, enabling understanding of "upside" and "downside" scenarios.

Decision Trees: Maps decisions and possible outcomes, assigning probabilities and values to each path. Useful for complex decisions with multiple branching options.

Quantitative assessment requires detailed data and expertise but provides precise risk exposure measurements.

Risk Mitigation Strategies: Taking Action

Once risks are assessed and prioritized, mitigation strategies should be developed and implemented.

Four Primary Risk Response Strategies

Avoidance: Eliminate the risk by changing project approach, scope, or objectives. Example: Rather than selecting unproven technology introducing adoption risk, use proven technology accepted by community and team. Avoidance removes risk entirely but may eliminate opportunities or increase other costs.

Mitigation (or Reduction): Take actions reducing probability of risk occurrence or impact if it occurs. Examples: Conduct technical proofs-of-concept reducing technology selection uncertainty, implement robust testing reducing quality risk, hire additional resources reducing schedule risk. Most risks are addressed through mitigation—accepting that risks exist but working to minimize them.

Transfer: Shift risk to external parties through contractual agreements or insurance. Examples: Outsource risky components to specialists with expertise in addressing that risk, purchase insurance covering potential losses, include contractual clauses transferring responsibility for third-party failures. Transfer works well for financial risks but less well for technical or organizational risks.

Acceptance (or Tolerance): Accept that risk exists, prepare contingency responses, and proceed. Appropriate for low-probability/low-impact risks where mitigation cost exceeds benefit. Acceptance requires formal acknowledgment and contingency planning—not ignoring risks.

Developing Mitigation Strategies for High-Priority Risks

High-priority risks warrant detailed mitigation strategies including:

Specific Actions: Clearly articulate specific steps to reduce probability or impact. "Reduce technical risk" is vague; "Conduct two-week technical proof-of-concept validating scalability and evaluating performance" is specific and actionable.

Resource Requirements: Identify people, budget, and time required to implement mitigation. Mitigation strategies consuming resources must be explicitly planned and budgeted.

Success Measures: Define how you'll know mitigation is working. How will you measure that technical risk decreased? That communication improved? Specific measures enable monitoring progress.

Timeline: When will mitigation activities occur? Early in project when risk impact would be greatest, or later when more information enables informed decisions?

Owner: Who is responsible for implementing and monitoring this mitigation?

Common Mitigation Approaches

For Technical Risks:

• Conduct technical proofs-of-concept or spikes to reduce technology selection uncertainty
• Design for flexibility enabling easier changes if requirements evolve
• Plan regular architecture reviews ensuring design remains appropriate
• Implement comprehensive testing reducing quality risks

For Schedule Risks:

• Break work into smaller tasks enabling more accurate estimation
• Allocate buffer time (schedule contingency) for high-uncertainty tasks
• Identify critical path and manage dependencies carefully
• Track progress rigorously enabling early detection of schedule slips

For Budget Risks:

• Conduct detailed cost estimation involving team expertise
• Allocate budget contingency for high-uncertainty estimates
• Implement earned value management tracking actual spending against budget
• Control scope rigorously preventing cost-increasing changes

For Organizational Risks:

• Invest in team training and skill development
• Cross-train team members enabling knowledge sharing
• Implement documentation preventing knowledge loss from departures
• Establish clear communication channels and feedback mechanisms

For External Risks:

• Diversify vendor relationships reducing dependency on single sources
• Negotiate contractual terms addressing contingency scenarios
• Monitor market conditions and regulatory changes
• Maintain flexibility in architecture enabling adaptation to external changes

Risk Monitoring and Control

Mitigation strategies don't implement themselves. Active monitoring verifies mitigation effectiveness and detects when risks materialize.

Establishing Monitoring Approach

Risk Owners: Assign ownership for each significant risk. Risk owners monitor their assigned risks, track effectiveness of mitigation strategies, and escalate when risks require attention.

Monitoring Frequency: Establish monitoring cadence. Daily monitoring may be appropriate for critical risks; weekly monitoring for moderate risks; monthly review for lower-priority risks. Monitoring should be frequent enough to enable timely response but not so frequent as to create administrative overhead.

Metrics and Indicators: Define specific metrics indicating risk health:

• Schedule variance (actual progress vs. planned progress)
• Budget variance (actual spending vs. budgeted spending)
• Test results (defect trends)
• Code quality metrics (complexity, coverage)
• Stakeholder satisfaction surveys
• Team velocity trends
• Technical debt accumulation

Escalation Triggers: Define conditions requiring escalation to senior leadership. If schedule slips exceed 2 weeks, if budget overruns exceed 10%, if critical defect discovery rate increases—specific triggers enable consistent escalation.

Risk Review Meetings

Regular risk review meetings maintain shared awareness and enable collaborative problem-solving.

Weekly Status Meetings: Incorporate brief risk updates into weekly status meetings. Each risk owner provides 1-2 minute updates on assigned risks: current status, any changes in probability or impact, mitigation progress, and any issues requiring team attention.

Sprint Reviews: In agile projects, include risk review in sprint reviews. Discuss risks surfaced during sprint, reassess existing risks, and adjust mitigation strategies based on recent learning.

Monthly Risk Reviews: Conduct formal monthly risk reviews specifically focused on comprehensive risk assessment. Update risk register, discuss risk trends, review effectiveness of mitigation strategies, and identify emerging risks.

Post-Incident Reviews: When risks materialize into incidents, conduct post-incident reviews examining what happened, why mitigation wasn't adequate, and how to prevent recurrence.

Risk Reporting

Communicate risk status to stakeholders through structured reporting:

Executive Dashboard: One-page visualization showing risk health at organizational level. High-priority risks highlighted; mitigation status indicated; overall risk trend shown.

Detailed Risk Report: Comprehensive report for project managers and technical leads covering all risks, individual statuses, mitigation progress, and emerging issues.

Stakeholder Communication: Tailor risk communication to stakeholder interests. Business stakeholders focus on business and schedule risks; technical leadership focuses on technical and organizational risks; operations focuses on deployment and operational risks.

Risk Management in Agile Environments

Traditional risk management approaches sometimes conflict with agile values. Agile projects require adapted risk management practices.

Integrating Risk into Agile Processes

Risk in Product Backlog: Represent risks as backlog items or epics. Include risk mitigation activities—"Spike: Technical proof-of-concept validating scalability"—in sprint planning and prioritization alongside feature development.

Risk in Sprint Planning: Discuss potential risks during sprint planning. How might selected stories introduce risk? What technical uncertainties exist? What could prevent sprint completion? This discussion surfaces risks early within sprints.

Risk in Daily Standups: Brief risk updates in daily standups enable rapid escalation. If a team member discovers unanticipated complexity introducing risk, the team learns immediately and can adjust approach.

Risk in Sprint Reviews: Risk review at sprint conclusion enables learning. Did risks materialize during sprint? Were mitigation strategies effective? What new risks emerged?

Agile Risk Mitigation Techniques

Early Completion of Risky Work: Complete stories with highest technical uncertainty early in sprints. This surfaces integration issues while sprint time remains for response.

Continuous Integration and Testing: Frequent integration (daily or multiple times daily) and comprehensive automated testing reduce quality risk and enable rapid detection of problems.

Technical Debt Management: Address technical debt before it becomes crisis. Allocate sprint capacity (15-30%) to technical debt reduction preventing accumulation.

Incremental Delivery: Deliver working increments of software frequently. Early delivery enables real-world feedback reducing requirements risk and enabling course correction.

Stakeholder Engagement: Continuous stakeholder engagement through demos and reviews reduces requirements misalignment risk. Early discovery of misalignment enables adjustment.

AI and Advanced Techniques in Risk Management

Modern organizations increasingly leverage artificial intelligence and advanced techniques to enhance risk management capabilities.

Machine Learning for Risk Prediction

Machine learning models trained on historical project data can predict risks with significant accuracy. Models analyze features like project size, team composition, technology selections, schedule pressure, and requirement change rates to identify projects at elevated risk.

Recent research shows AI-enhanced risk prediction achieves 94% accuracy in risk identification compared to 75-80% for traditional approaches. When used appropriately, AI becomes force multiplier, enabling human judgment to focus where it's most valuable.

Automated Risk Monitoring

Tools increasingly automate risk monitoring, continuously analyzing project metrics and identifying emerging risks without waiting for manual review cycles. Automated alerts when schedule variance exceeds thresholds or when defect discovery accelerates enable rapid response.

Quantitative Risk Assessment

Monte Carlo simulations and other quantitative techniques now run on standard hardware, enabling sophisticated probability analysis. Organizations can model realistic scenarios considering interdependencies among risks and understanding probability distributions of outcomes.

Practical Risk Management Implementation

Implementing systematic risk management requires structured approaches.

Phase 1: Risk Management Foundation (Weeks 1-2)

• Define organizational risk management policy
• Identify risk management roles and responsibilities
• Establish risk register template and tools
• Conduct risk management training for team

Phase 2: Initial Risk Assessment (Weeks 3-4)

• Conduct comprehensive risk identification
• Populate risk register
• Assess probability and impact
• Create probability-impact matrix

Phase 3: Mitigation Planning (Weeks 5-6)

• Develop mitigation strategies for high-priority risks
• Identify resource requirements
• Define success measures
• Assign risk owners

Phase 4: Monitoring and Control (Ongoing)

• Implement monitoring processes
• Conduct regular risk reviews
• Track mitigation effectiveness
• Escalate emerging issues
• Update risk register

Phase 5: Continuous Improvement (Ongoing)

• Review effectiveness of risk management processes
• Learn from risks that materialized
• Adjust approaches based on experience
• Share organizational learnings

Tools and Technologies for Risk Management

Modern tools enable systematic risk management:

Risk Management Platforms: Tools like LogicManager, Resolver, and MetricStream provide centralized repositories for risk registers, automated monitoring, and integrated reporting.

Project Management Tools: Jira, Azure DevOps, and Monday.com increasingly include risk management features enabling integration of risk management with project planning and tracking.

Spreadsheet-Based Approaches: Many organizations start with spreadsheets (Excel, Google Sheets) enabling risk register creation without significant investment. While less sophisticated than specialized tools, spreadsheets often suffice for small to medium projects.

Business Intelligence and Analytics: Advanced organizations leverage business intelligence tools to visualize risk trends, perform predictive analytics, and integrate risk data with other business metrics.

Common Risk Management Pitfalls

Organizations implementing risk management often encounter predictable challenges:

Risk Management Theater: Creating risk registers and processes without genuine commitment to risk response. When identified risks are ignored or mitigation strategies are deprioritized, risk management becomes administrative overhead rather than value-creating practice.

Over-Fixation on Negative Risks: Traditional risk management focuses exclusively on threats. Many opportunities exist to be missed if not explicitly addressed. Balance threat management with opportunity management.

Insufficient Risk Ownership: Assigning risks without ensuring owners understand their responsibilities or have authority to implement responses. Risk management succeeds when owners have genuine accountability.

Reactive Rather than Proactive: Failing to implement mitigation until risks materialize. By then, response costs are much higher. Proactive mitigation prevents many risks from occurring.

Inadequate Monitoring: Risk registers created during planning but then neglected. Risks requiring no monitoring at project start may become critical as circumstances change. Regular review ensures relevance.

Conclusion

Risk management is not insurance against project failure; it's systematic preparation for predictable uncertainties. No risk management approach prevents all problems. However, organizations implementing systematic risk management complete projects on time, on budget, and with higher quality far more frequently than organizations treating risk management as optional.

Effective risk management requires sustained commitment: systematic identification surfacing potential problems, thoughtful assessment prioritizing management effort, deliberate mitigation reducing probability or impact, and active monitoring enabling course correction. It requires organizational culture valuing risk awareness and collaborative problem-solving over blame assignment and crisis management.

The most resilient organizations don't avoid risks—that's impossible in complex software development. Instead, they understand risks deeply, prepare responses, and adapt when necessary. They view risk management not as constraint on agility but as enabler of sustainable, predictable delivery.

Implement the approaches discussed in this guide—establish risk ownership, maintain living risk registers, conduct regular monitoring, and create organizational capacity for proactive response—and you transform risk from project killer into manageable dimension of software delivery.

References

1. IEEE Xplore. (2024). Data-driven Strategies for Enhanced Risk Management Performance in Software Development Perspective: an Agile Implementation. Retrieved from https://ieeexplore.ieee.org/document/10872763/

2. IEEE Xplore. (2024). Exploring Innovative Approaches for Software Development Risk Assessment and Management. Retrieved from https://ieeexplore.ieee.org/document/10730152/

3. ACM Digital Library. (2024). SERGE - Serious Game for the Education of Risk Management in Software Project Management. Retrieved from https://dl.acm.org/doi/10.1145/3639474.3640085

4. IEEE Xplore. (2024). Technology Acquisition Plans to Foster Supply Chain Risk Management Learning Outcomes in Project-Based Software Development Courses. Retrieved from https://ieeexplore.ieee.org/document/10663048/

5. IEEE Xplore. (2025). Machine Learning based Effort Estimation Models for Software Development Projects related Datasets with diverse features. Retrieved from https://ieeexplore.ieee.org/document/10932601/

6. Wiley Online Library. (2024). Enhancing Agile Project Success: A Comprehensive Study of Risk Management Approaches Among Malaysian Practitioners. Retrieved from https://onlinelibrary.wiley.com/doi/10.1002/smr.2681

7. Goldncloud Publications. (2024). Risk Management Framework: Identifying and Mitigating Business Risks. Retrieved from https://goldncloudpublications.com/index.php/irjaem/article/view/303

8. arxiv.org. (2024). Future of Artificial Intelligence in Agile Software Development. Retrieved from https://arxiv.org/abs/2408.00703

9. ACM Digital Library. (2024). Technical Debt Management in Agile Software Development: A Systematic Mapping Study. Retrieved from https://dl.acm.org/doi/10.1145/3701625.3701669

10. MDPI. (2021). Resource Optimization-Based Software Risk Reduction Model for Large-Scale Application Development. Retrieved from https://www.mdpi.com/2071-1050/13/5/2602/pdf

11. arxiv.org. (2021). Managing Layers of Risk: Uncertainty in Large Development Programs Combining Agile Software Development and Traditional Project Management. Retrieved from https://arxiv.org/pdf/2103.09034.pdf

12. MDPI. (2025). AI-Driven Decision Support Systems in Agile Software Project Management: Enhancing Risk Mitigation and Resource Allocation. Retrieved from https://www.mdpi.com/2079-8954/13/3/208

13. IEEE Xplore. (2025). Empirical Software Risk Calculation with Software Risk Factors. Retrieved from https://ieeexplore.ieee.org/document/11155755/

14. ITToolkit. (2025). Project Risk Management: Complete Guide for 2025 Success. Retrieved from https://www.ittoolkit.com/project-risk-management-complete-guide-for-2025-success/

15. Software Seni. (2024). Integrating Risk Management into Agile Development Processes. Retrieved from https://www.softwareseni.com/integrating-risk-management-into-agile-development-processes/

16. SoftTeco. (2025). Understanding Risk Analysis in Software Engineering. Retrieved from https://softteco.com/blog/risk-analysis-in-software-engineering

17. BrowserStack. (2024). How to Perform Software Risk Assessment. Retrieved from https://www.browserstack.com/guide/software-risk-assessment

18. Agent Studio. (2024). Top 15 Software Project Risks and Mitigation Examples in 2024. Retrieved from https://agentestudio.com/blog/software-development-project-risks

19. GeeksforGeeks. (2020). Different Types of Risks in Software Project Development. Retrieved from https://www.geeksforgeeks.org/software-engineering/different-types-of-risks-in-software-project-development/

20. KVY Technology. (2024). Risk Management in Software Development in 2024. Retrieved from https://kvytechnology.com/blog/software/risk-management-in-software-development/