Computer Security App Development

Security software is the most adversarial category in engineering: the system must correctly classify every threat in real time while the threat surface changes continuously and attackers actively probe for gaps. Scrums.com builds dedicated engineering teams for companies developing security platforms: SIEM and SOAR products, endpoint detection and response (EDR) tools, identity security infrastructure, vulnerability management systems, and the threat intelligence pipelines that connect them.

Security Event Ingestion and Threat Detection Architecture

The foundation of any security platform is the ability to ingest high-volume event streams from heterogeneous sources and detect malicious patterns faster than analysts can manually review them.

Event ingestion uses a Kafka-based pipeline with per-source topic partitioning: agent telemetry (endpoint events), network flow data, cloud audit logs (AWS CloudTrail, GCP Audit Logs, Azure Activity Logs), and identity provider events (Okta, Azure AD) are ingested as separate streams and normalised to a canonical event schema (a superset of OCSF: Open Cybersecurity Schema Framework) before being written to the immutable event store. Schema normalisation at ingestion time means detection rules operate against a consistent data model regardless of source.

Detection rules are implemented at three layers. Streaming detections (Flink or Kafka Streams) evaluate each event against stateless or low-state rules: known-bad IP lookups, hash-based malware matches, impossible travel (login from two geographies within an impossible timeframe). Correlation detections operate over sliding time windows: brute-force sequences (10 failed logins in 60 seconds from the same IP), lateral movement patterns (service account authentication from an unusual host following a privilege escalation event). ML-based detections (isolation forest, autoencoder) evaluate entity behaviour baselines and flag deviations: a user accessing 100x their usual data volume, a service account making API calls at 3am for the first time.

Alerts are not raw events; they are enriched entities. An alert record carries the triggering events (by ID), a severity score, the affected asset's classification (crown jewel, standard, unknown), threat intelligence enrichment (VirusTotal hash lookup, IP reputation, MITRE ATT&CK tactic and technique mapping), and a deduplication key to prevent alert storms from a single root cause generating hundreds of separate tickets.

Identity Security and Access Control Infrastructure

Identity is the primary attack surface in modern enterprise environments. Credential-based attacks account for the majority of successful breaches, and security platforms built without a deep identity data model cannot detect the most common attack patterns.

The identity graph models entities (users, service accounts, devices, applications) as nodes and their relationships (owns, authenticates_with, member_of, delegates_to) as edges. Authentication events are appended to an authentication_events ledger keyed by entity_id. Behavioural baselines per entity (typical login hours, typical source IPs, typical accessed resources) are computed as rolling aggregates. Deviations from baseline trigger enriched alerts rather than raw log entries.

Privileged access management (PAM) integration tracks just-in-time privilege grants: when an engineer requests elevated access, the platform creates a privilege_grant record with a TTL, scope (what resources, what actions), and the approval chain. The grant is automatically revoked at expiry. Every action taken under the grant is tagged with the grant ID, creating an immutable privileged-session audit trail. Zombie grant detection (grants that were never used, or expired but not technically revoked in the downstream system) runs as a daily scheduled detection.

Zero trust enforcement signals flow from the security platform to the policy enforcement layer (Zscaler, Cloudflare Access, or a native reverse proxy): when the platform detects a compromised credential or suspicious session, it can automatically push a block instruction to the enforcement layer via API, reducing the time from detection to containment from hours to seconds.

Security platforms like these are built and delivered by dedicated teams through our mobile app development service.

Vulnerability Management and Patch Intelligence

Threat detection catches attacks in progress. Vulnerability management reduces the attack surface before attackers reach it.

The asset inventory is the foundation: a continuously reconciled map of all assets (hosts, containers, cloud resources, software packages) and their versions. Asset discovery runs via agent-based telemetry (endpoint agents report installed software and versions), agentless network scanning (Nmap-based), and cloud API enumeration (AWS EC2/Lambda, Azure VMs, GCP Compute). Discrepancies between what infrastructure-as-code declares and what the scanner finds are surfaced as a shadow_asset alert: unmanaged assets are a common attack vector.

Vulnerability correlation joins the asset inventory against CVE feeds (NVD, vendor advisories, CISA KEV: Known Exploited Vulnerabilities catalogue). A vulnerability_instance record is created for each (asset, CVE) pair, carrying the CVSS base score, the EPSS score (Exploit Prediction Scoring System: the probability that the vulnerability will be exploited in the wild within 30 days), and whether the CVE appears in the CISA KEV catalogue. Prioritisation is driven by EPSS plus KEV membership, not CVSS alone: a critical CVSS score on a vulnerability with no known exploit and no internet exposure is lower priority than a medium CVSS score on a KEV-listed vulnerability running on an internet-facing asset.

Patch status tracking integrates with the organisation's patch management system (SCCM, Intune, AWS Systems Manager Patch Manager) via API to record when a patch was applied, by whom, and whether the vulnerability instance was closed. Unpatched KEV-listed vulnerabilities on internet-facing assets escalate automatically from medium to critical in the SOC queue after 30 days.

Incident Response Automation and SOAR Integration

Manual incident response does not scale. The mean time to respond (MTTR) to a security incident is a direct function of how much of the response workflow is automated.

Playbooks are defined as configuration (DAG-based): trigger condition, investigation steps (look up the entity in the identity graph, check recent authentication events, query threat intelligence for the source IP), containment actions (isolate the endpoint via EDR API, revoke the OAuth token via IdP API, block the IP at the firewall), and notification steps (PagerDuty for SOC on-call, Slack alert to the security channel, Jira ticket for the asset owner). Playbook steps that require human approval create a pending_approval task with a deadline; steps that time out without approval escalate automatically.

The SOAR engine integrates with the security toolchain via a connector library: EDR vendors (CrowdStrike, SentinelOne, Carbon Black), identity providers (Okta, Azure AD, Ping), network controls (Palo Alto, Cisco ASA, Cloudflare), ticketing systems (Jira, ServiceNow), and communication tools (Slack, Teams, PagerDuty). Each connector is an independently versioned module so that vendor API changes can be patched without redeploying the core SOAR engine.

Evidence collection runs automatically at alert creation: the SOAR engine snapshots the relevant event window, the entity's recent activity, the affected asset's configuration state, and the applicable threat intelligence context into an incident_evidence package. Analysts start triage with full context rather than spending the first 20 minutes of a P1 incident collecting logs from disparate systems.

Ready to build a security platform? Start a conversation with Scrums.com, or explore our dedicated team model.