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Introduction:
From the CrowdStrike logic error that crashed 8.5 million Windows devices in July 2024 to the Jaguar Land Rover cyberattack that halted UK car production and cost an estimated $2.5 billion in 2025, modern disruptions no longer follow predictable, linear paths. These incidents exposed a critical vulnerability: most organisations still rely on static service maps and siloed spreadsheets to understand how their services are delivered. When a single supplier update or ransomware event triggers cascading failures across technology, third parties, and supply chains, these offline documents become obsolete within hours. KPMG Service Delivery Resilience Intelligence (SDRI), powered by Quantexa’s Decision Intelligence Platform, addresses this gap by creating a living, connected view of service delivery models—revealing hidden dependencies, concentration risks, and single points of failure before they escalate into crises.
Learning Objectives:
- Understand how entity resolution and graph analytics transform siloed operational data into dynamic, actionable resilience intelligence.
- Learn to identify single points of failure and concentration risks across technology stacks, third-party suppliers, and internal dependencies.
- Acquire practical commands and methodologies for mapping dependencies, auditing API supply chains, and hardening cloud architectures against systemic disruptions.
You Should Know:
- Dependency Mapping Beyond Point-to-Point: Building a Dynamic Service Graph
Traditional Business Impact Analyses (BIAs) and service maps treat dependencies as static, one-to-one relationships—Application A depends on Database B. In reality, a single critical service may span dozens of microservices, multiple cloud providers, third-party APIs, data pipelines, and physical premises. The CrowdStrike outage demonstrated this perfectly: a defective content update pushed to Falcon sensors created a logic error that crashed Windows systems globally, grounding flights, taking TV broadcasts off air, and forcing hospitals to cancel operations. No static spreadsheet could have modelled that cascade in real time.
SDRI solves this by ingesting structured and unstructured data—technology inventories, supplier contracts, people, premises, and telemetry—and deploying advanced analytics to uncover invisible dependencies. The result is a continuously refreshed graph that models how services, networks, and dependencies interact.
Step‑by‑step guide to building a dynamic dependency graph:
- Inventory all assets: Collect CMDB exports, cloud asset inventories (AWS Config, Azure Resource Graph), and third-party supplier lists.
- Define critical services: Identify the 10–20 services that are most important to customers and regulators.
- Map direct dependencies: For each critical service, list all technology components, data sources, people, and suppliers that directly enable it.
- Discover indirect dependencies: Use entity resolution to link seemingly unrelated data—e.g., two different applications that share the same underlying cloud region or the same subcontractor.
- Visualise as a graph: Generate a network graph where nodes are assets and edges represent dependencies. Tools like Neo4j, Amazon Neptune, or Quantexa’s Knowledge Graph can render these relationships.
- Refresh continuously: Automate data ingestion to keep the graph current—static maps are obsolete the moment they are printed.
Linux/Windows Commands for Asset Discovery:
Linux: Discover listening services and their dependencies ss -tulpn | grep LISTEN lsof -i -P -1 | grep LISTEN Linux: Map file dependencies of a critical binary ldd /usr/bin/nginx Windows: List all installed services and their dependencies sc query state= all | findstr SERVICE_NAME sc qc <ServiceName> Shows dependencies for a specific service Windows: Export all running processes with their parent relationships wmic process get processid,parentprocessid,executablepath
- Entity Resolution: Breaking Down Data Silos to Reveal Hidden Risks
Organisations typically store operational data in disconnected systems—IT service management, procurement, HR, and security tools each maintain their own records. A single supplier might appear as “AWS” in one system, “Amazon Web Services” in another, and “Amazon” in a third. Without entity resolution, these records remain siloed, and concentration risks go undetected.
Quantexa’s entity resolution technology addresses this by applying machine learning and AI to link disparate records into unified 360-degree views of counterparties, suppliers, and internal assets. It can identify up to 20% duplication in customer records and enrich data across sources. For operational resilience, this means discovering that multiple critical services all depend on the same single cloud region, the same subcontractor, or the same end-of-life technology component—concentrations that would remain invisible in siloed data.
Step‑by‑step guide to entity resolution for resilience:
- Collect data from all silos: Export supplier lists from procurement, asset inventories from IT, people data from HR, and premises data from facilities.
- Standardise fields: Normalise naming conventions—e.g., map “AWS”, “Amazon Web Services”, and “Amazon” to a single entity ID.
- Apply entity resolution rules: Use fuzzy matching, deterministic matching, and ML-based clustering to link records that refer to the same real-world entity.
- Build a knowledge graph: Link resolved entities to the services, applications, and data flows they support.
- Query for concentration risks: Run graph queries to find entities that have high betweenness centrality—i.e., single points of failure that, if disrupted, would break multiple critical services.
Example graph query (Cypher-like syntax) to find single points of failure:
MATCH (s:Service)-[:DEPENDS_ON]->(a:Asset) WITH a, COUNT(s) AS service_count WHERE service_count > 5 RETURN a.name, service_count ORDER BY service_count DESC
3. API Supply Chain Security: Mapping Third-Party Dependencies
Modern applications are assemblies of APIs—payment gateways, identity providers, messaging queues, and AI model endpoints. Each API introduces a dependency that can become a single point of failure. The Jaguar Land Rover attack in 2025, which halted production at UK plants and disrupted dealerships worldwide, underscored how a single compromised supplier can cripple an entire ecosystem. Lower-tier suppliers in the JLR supply chain faced acute financial strain, with many lacking resources to withstand prolonged disruption.
SDRI addresses this by modelling API dependencies as first-class entities in the service graph. Organisations can map which internal services call which external APIs, what authentication mechanisms are used, and what the contractual SLAs are.
Step‑by‑step guide to auditing API supply chain dependencies:
- Discover all external API calls: Use network monitoring, API gateways logs, or eBPF-based tools to capture outbound API traffic.
- Inventory API endpoints: List all third-party APIs your applications call, including version numbers and authentication methods.
- Map criticality: For each API, determine which business services depend on it and what the impact would be if it became unavailable.
- Assess concentration: Identify APIs that are called by multiple critical services—these are high-risk single points of failure.
- Implement fallbacks: For critical APIs, design circuit breakers, retries with exponential backoff, and fallback to cached responses or alternative providers.
Linux/Windows Commands for API Dependency Discovery:
Linux: Capture outbound HTTP/HTTPS traffic to discover API calls
sudo tcpdump -i any -1 'tcp port 80 or tcp port 443' -A | grep -i "host:"
Linux: Use ss to see established outbound connections
ss -tupn | grep ESTAB | grep -v 127.0.0.1
Windows: Use netstat to see active connections
netstat -ano | findstr ESTABLISHED
Windows: Use PowerShell to get all outbound connections
Get-1etTCPConnection | Where-Object {$_.State -eq 'Established'} | Select-Object LocalAddress, LocalPort, RemoteAddress, RemotePort
4. Cloud Hardening Against Systemic Failures
Cloud architectures introduce their own resilience challenges. A single misconfigured security group, an overly permissive IAM role, or a shared tenancy vulnerability can cascade across services. The CrowdStrike incident, while not a cloud misconfiguration, highlighted how a single update can affect millions of systems simultaneously. In cloud environments, concentration risks manifest as shared availability zones, common network segments, or single identity providers.
Step‑by‑step guide to cloud hardening for resilience:
- Map cloud assets: Use cloud provider APIs to inventory all resources—EC2 instances, S3 buckets, RDS databases, Lambda functions, and Kubernetes clusters.
- Identify single points of failure: Look for resources that are not deployed across multiple availability zones or regions.
- Audit IAM policies: Ensure least-privilege access and remove unused roles and permissions.
- Implement immutable backups: Combine dependency maps with immutable backups to enable organised, logical recovery rather than chaotic restoration.
- Automate failover: Use infrastructure-as-code (Terraform, CloudFormation) to define and test disaster recovery procedures.
AWS CLI Commands for Resilience Auditing:
List all EC2 instances and their availability zones aws ec2 describe-instances --query 'Reservations[].Instances[].[InstanceId,Placement.AvailabilityZone]' List all RDS instances and their multi-AZ status aws rds describe-db-instances --query 'DBInstances[].[DBInstanceIdentifier,MultiAZ]' List all S3 buckets and their region aws s3api list-buckets --query 'Buckets[].[Name,CreationDate]' Audit IAM roles with overly permissive policies aws iam list-roles --query 'Roles[].[RoleName,AssumeRolePolicyDocument]'
- Incident Response and Crisis Preparation with Dynamic Intelligence
When disruption strikes, speed matters. Organisations with static resilience documentation often scramble, elevating incidents from inconvenience to crisis. SDRI enables organisations to move from reactive to proactive resilience management by providing actionable insights for incident response, crisis preparation, and regulatory engagement.
Step‑by‑step guide to using dynamic intelligence in incident response:
- Pre‑incident: Run scenario tests using the service graph—simulate the failure of a critical supplier, a cloud region, or a key API, and observe the predicted cascade.
- During incident: Use the graph to instantly identify all services impacted by a failure and the teams responsible for each.
- Communicate: Generate impact summaries for regulators, boards, and customers based on real-time data.
- Post‑incident: Update the graph with learnings from the incident to improve future resilience.
Linux/Windows Commands for Incident Response:
Linux: Quick system health check
top -b -1 1 | head -20
df -h
free -m
Linux: Check for failed services
systemctl --failed
Windows: Check event logs for critical errors
Get-WinEvent -LogName System -MaxEvents 50 | Where-Object {$_.LevelDisplayName -eq 'Error'}
Windows: Check service status
Get-Service | Where-Object {$_.Status -eq 'Stopped'}
What Undercode Say:
- Key Takeaway 1: Static resilience documentation is a liability in today’s interconnected threat landscape. The CrowdStrike and JLR incidents prove that disruptions cascade through invisible dependencies—and organisations that cannot see these connections will always be reacting, not preventing.
- Key Takeaway 2: Entity resolution and graph analytics are no longer optional for large enterprises. By breaking down data silos and revealing concentration risks, these technologies transform operational resilience from a compliance exercise into a strategic capability that protects revenue, reputation, and regulatory standing.
The shift from static to dynamic resilience intelligence represents a fundamental change in how organisations approach risk. Traditional BIAs and service maps were designed for a world where dependencies were linear and predictable. That world no longer exists. Modern organisations operate in a web of interconnected technology, suppliers, and AI systems—and resilience must be modelled as a living graph, not a dead spreadsheet. SDRI, powered by Quantexa, offers a practical path forward by ingesting real-world data, resolving entities across silos, and delivering continuous, actionable intelligence. The organisations that adopt this approach will recover faster, make better investment decisions, and earn the trust of regulators and customers alike. Those that cling to static documentation will find themselves scrambling when the next CrowdStrike or JLR-scale disruption hits.
Prediction:
- +1 By 2028, dynamic dependency mapping will become a regulatory requirement for financial services and critical infrastructure operators, mirroring the evolution of DORA and the UK Operational Resilience policy.
- +1 AI-driven entity resolution will reduce mean time to detect (MTTD) supply chain risks by over 60%, as graph analytics automatically flag concentration risks before they manifest as outages.
- -1 Organisations that delay adopting dynamic resilience intelligence will face increasingly severe regulatory fines and shareholder lawsuits, as boards are held personally accountable for failing to understand their service delivery dependencies.
- +1 The convergence of operational resilience and cyber resilience will accelerate, with security teams and business continuity teams sharing a unified data graph that models both threat vectors and service dependencies.
- -1 The complexity of maintaining dynamic graphs will create new challenges—data quality, governance, and update frequency will become critical success factors, and organisations that neglect these will trade one set of blind spots for another.
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