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Tech Mahindra - Attack Surface Management
Vendor Analysis
published on Feb 20, 2025
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Report Overview:
This NelsonHall vendor assessment analyzes Tech Mahindra's offerings and capabilities in Attack Surface Management
Who is this Report for:
NelsonHall’s Attack Surface Management (ASM) profile on Tech Mahindra is a comprehensive assessment of Tech Mahindra’s offerings and capabilities, designed for:
- Sourcing managers monitoring the capabilities of existing suppliers of attack surface management and identifying vendor suitability for attack surface management RFPs
- Vendor marketing, sales, and business managers looking to benchmark themselves against their peers
- Financial analysts and investors specializing in the attack surface management sector.
Scope of this Report:
The report provides a comprehensive and objective analysis of Tech Mahindra’s attack surface management offerings, capabilities, and market and financial strengths, including:
- Identification of the company’s strategy, emphasis, and new developments
- Analysis of the company’s strengths, weaknesses, and outlook
- Revenue estimates
- Analysis of the profile of the company’s customer base including the company’s targeting strategy and examples of current contracts
- Analysis of the company’s offerings and key service components
- Analysis of the company’s delivery organization including the location of delivery centers.
Key Findings & Highlights:
Tech Mahindra divides its ASM+ services into three main categories:
- Reconnaissance
- Penetration testing
- Red teaming.
Tech Mahindra offers the individual ASM services as packages; clients can leverage reconnaissance alone, add on penetration testing (priced by the number of assets being tested with tiered pricing), and red teaming.
Tech Mahindra's reconnaissance services use the company's ASM+ engine, framework, and playbooks to locate assets and identify vulnerabilities in the configurations of these assets. Following this discovery of assets using the ASM+ engine, Tech Mahindra performs active recon, which passes the full list of assets to the engine's supervised learning ML to discover further assets through directory brute force scanning and reduce the number of false positives through active service fingerprinting and scanning.