Risk Management

Risk management is essential for Sentinel because the project combines AI reasoning, DevOps automation, observability tools, cloud-style infrastructure, and real-time incident handling. The following section identifies the main risks associated with the development and operation of the system, evaluates them through a probability-impact matrix, assigns responsibilities using a RACI matrix, and defines mitigation strategies for each risk.

Risk Inventory

Code	Risk	Description
R1	Incorrect incident classification	Sentinel may classify an incident under the wrong technical category, which could lead the investigation process in the wrong direction.
R2	Incomplete evidence collection	The system may fail to collect enough logs, metrics, or alerts, making the root cause analysis incomplete.
R3	Dashboard update issues	The real-time dashboard may not update incident status, evidence, or recommendations correctly.
R4	Inaccurate AI recommendations	The AI agent may suggest a remediation action that is not fully appropriate for the incident context.
R5	Observability integration failure	External tools such as monitoring, logging, or alerting services may be unavailable or incorrectly configured.
R6	Loss of incident context	Sentinel may lose important information about the current incident, such as previous actions, evidence, or current phase.
R7	Irrelevant runbook retrieval	The knowledge base may return outdated or unrelated runbooks, reducing the quality of the recommendation.
R8	Unauthorized access to incident data	Sensitive operational information, such as logs or incident details, may be exposed if access control is not properly implemented.
R9	External AI service outage	The LLM provider (OpenAI) may experience a full outage or enforce rate limits that completely block Sentinel's reasoning pipeline, making the system unable to classify or analyze any incident.
R10	Sprint delay due to technical complexity	The integration of multiple components may increase development complexity and delay Sprint 2 deliverables.
R11	API key exposure	OpenAI or Supabase credentials accidentally committed to the repository or leaked through logs may result in unauthorized usage, financial loss, or a data breach.

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Probability and Impact Matrix

Probability / Impact	Negligible	Marginal	Moderate	Critical	Catastrophic
Certain
Very Likely		R3	R10		R9
Possible			R1, R2, R7	R4, R5, R6
Unlikely				R8, R11
Rare

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Risk Level Summary

Code	Probability	Impact	Risk Level
R1	Possible	Moderate	Moderate Risk
R2	Possible	Moderate	Moderate Risk
R3	Very Likely	Marginal	Low Risk
R4	Possible	Critical	Moderate Risk
R5	Possible	Critical	Moderate Risk
R6	Possible	Critical	Moderate Risk
R7	Possible	Moderate	Moderate Risk
R8	Unlikely	Critical	Moderate Risk
R9	Very Likely	Catastrophic	Extreme Risk
R10	Very Likely	Moderate	Moderate Risk
R11	Unlikely	Critical	Moderate Risk

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RACI Matrix

Risk Management Activity	Product Owner	Scrum Master	Tech Lead / Architect	Developers	QA / Tester	UX/UI
Identify project risks	A	R	C	C	C	I
Analyze probability and impact	C	R	A	C	C	I
Define mitigation strategies	C	R	A	C	C	I
Monitor technical risks during the sprint	I	C	A	R	C	I
Validate risks related to testing and quality	I	C	C	C	A/R	I
Review risks related to user experience	I	C	C	I	C	A/R
Communicate critical risks to the team	A	R	C	I	I	I
Update risk documentation in the Wiki	C	A	C	I	R	I

R = Responsible
A = Accountable
C = Consulted
I = Informed

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Mitigation Strategies

Code	Risk	Mitigation Strategy
R1	Incorrect incident classification	Define clear incident categories and validate the AI classification with test cases. Allow the user to manually correct the category if needed.
R2	Incomplete evidence collection	Add validation checks to confirm that logs, metrics, and alerts were collected before generating recommendations.
R3	Dashboard update issues	Test the dashboard with different incident states and include a manual refresh option as a fallback.
R4	Inaccurate AI recommendations	Require human approval before executing critical actions and show the evidence that supports each recommendation.
R5	Observability integration failure	Implement health checks for external services and display clear error messages when a tool is unavailable.
R6	Loss of incident context	Store the incident state after each workflow phase, including evidence, actions, decisions, and current status.
R7	Irrelevant runbook retrieval	Review and organize runbook content before indexing it, and test the knowledge base with sample incidents.
R8	Unauthorized access to incident data	Use authentication, role-based access control, secure environment variables, and avoid exposing secrets in the repository.
R9	External AI service outage	Define a fallback mode that keeps collected evidence visible and allows manual incident handling when OpenAI is unavailable. Monitor the OpenAI status page and configure backend alerts for API failures.
R10	Sprint delay due to technical complexity	Prioritize core MVP features, divide tasks into smaller issues, and review blockers during sprint meetings.
R11	API key exposure	Add .env and .env.local to .gitignore. Enable GitHub secret scanning on the repository. Rotate any exposed keys immediately and audit all commits before merging to main.

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Summary

The main risks for Sentinel are related to AI reliability, evidence collection, external integrations, incident state management, security, and development complexity. Since Sentinel supports DevOps engineers during incident response, the project must prioritize transparency, human approval, traceability, and strong testing. These mitigation strategies help reduce the probability and impact of failures.