TCS Travel and Hospitality Scandinavia

SAS

IT Operations Automation · Security and Compliance

SAS needed to modernize a fragmented security estate that lacked behavioral detection, 24-hour monitoring, centralized governance, and scalable response processes, making it harder for the airline to protect digital operations as cloud adoption and cyber threats increased.

Developed numerous active use cases (80% custom) aligned with the MITRE ATT&CK Framework
Implemented 24-hour monitoring for alerts and security incidents

Reported impact

Business outcome

SAS gained a more resilient, always-on security operating model with better visibility, faster detection and response, improved compliance reporting, and stronger control over cyber risk as it continues its digital-first airline transformation.

  • Compliance
  • Productivity Improvement
  • Risk Reduction

Solution

SAS and TCS expanded an existing IT relationship into an intelligence-driven cybersecurity operating model. TCS migrated SAS's SIEM and EDR tooling into a unified security platform, added 24-hour monitoring, and connected multiple threat intelligence platforms so indicators of compromise could be enriched automatically. On top of that foundation, the teams built anomaly-based detection playbooks, automated investigation workflows to reduce MTTD and MTTR, and integrated high-severity alerting with internal communications and email channels for faster escalation. Supporting controls such as an Attack Surface Management platform, centralized vulnerability dashboards, threat hunting on SIEM and EDR telemetry, and a client-based cloud proxy platform gave SAS broader visibility across infrastructure, network, and application risk. The result was a security operations capability that uses AI-driven detection and workflow automation as a core operating layer rather than an isolated pilot.

Data flow

Endpoint, infrastructure, network, and application telemetry is collected through EDR and SIEM, enriched automatically with threat intelligence, and processed by anomaly-based detection playbooks and automated investigation workflows. High-severity alerts are pushed into internal communications and email channels, while dashboards, attack-surface monitoring, and threat hunting use the same telemetry to guide remediation and compliance oversight.

Solution architecture

6 components · 4 layers
  1. Ingest
    • EDR Feeds endpoint telemetry and detection signals into SAS's modernized security operations flow.
    • threat intelligence platforms Enrich alerts with external intelligence and automate indicator-of-compromise context.
  2. Compute
    • anomaly-based detection playbooks Apply intelligence-driven detection logic and automated response paths across active use cases.
  3. Storage
    • SIEM Aggregates and correlates security telemetry inside the unified security platform.
  4. Governance
    • Attack Surface Management platform Monitors external exposure and supports proactive infrastructure hardening.
    • centralised vulnerability dashboards Give leadership and security teams enterprise-wide visibility into vulnerability closure and compliance.

Architecture clues

  • Existing SIEM and EDR solutions were migrated into a unified security platform.
  • High-severity alerts are connected directly to collaboration and email channels.
  • Threat intelligence is integrated for automated IoC enrichment.
  • Vulnerability dashboards and attack-surface management extend the same operating model into governance.

Evidence from the source

"Automated alert investigation workflows to optimise MTTD and MTTR"
"Developed numerous active use cases (80% custom) aligned with the MITRE ATT&CK Framework, along with several anomaly-based detection playbooks"
"Enabled 24-hour monitoring to reduce blind spots in detection and response"
"Integrated multiple threat intelligence platforms for automated IoC enrichment"
"Migrated existing SIEM and EDR solutions to a unified security platform"
Transparency Evidence & methodology 95/100 reference value · 95% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value95/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence95%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. TCS

    SAS Partners with TCS, AI-driven Cyber Resilience Takes Flight

    First-party
    https://www.tcs.com/what-we-do/services/cybersecurity/case-study/sas-partners-tcs-ai-driven-cyber-resilience
    Source type
    Vendor-published customer story
    Published
    Jul 1, 2026
    Supports
    • Customer
    • Industry
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence