Threat modelling and architecture review
We map assets, data flows and trust boundaries, identify likely attackers and rank attack paths. The result is a prioritised list of design changes and the reasons for each.
Service
We assess systems for exploitable weaknesses, design architectures that limit the damage of a breach, and build detection that your own team can operate.
The problem
Every cloud account, API, remote-access tool, IoT sensor and machine learning model is another way in. Plant networks that were once isolated now exchange data with business systems. Security teams are small, tools overlap, and alerts arrive without the context needed to act on them.
Regulation adds deadlines. The CERT-In directions of April 2022 require specified incidents to be reported within six hours and logs to be retained for 180 days. The Digital Personal Data Protection Act, 2023 requires reasonable security safeguards for personal data and notification of breaches.
A common failure is the assessment that ends as a PDF. Findings are listed, nobody owns them, and the same issues appear a year later. We start from a threat model, test what matters to your operation, and stay involved until fixes are verified.
What we deliver
Each item can be contracted separately or combined into one programme of work.
We map assets, data flows and trust boundaries, identify likely attackers and rank attack paths. The result is a prioritised list of design changes and the reasons for each.
Testing of web applications, APIs, mobile apps, networks and cloud configurations under written rules of engagement and explicit authorisation from the system owner.
Static and dynamic analysis, dependency and secrets scanning, software bills of materials and security code review, wired into your CI pipeline so that checks run on every change.
Least-privilege identity and access management, network segmentation, configuration baselines, infrastructure-as-code scanning, and backup restoration tests that prove recovery works.
Firmware analysis, secure boot, signed over-the-air updates and device identity for connected products. For plants: zone and conduit design under IEC 62443 and passive monitoring of control networks.
Log pipelines, detection rules mapped to MITRE ATT&CK, response playbooks and tabletop exercises. Where fixed rules fall short, we add anomaly detection models.
Technologies and methods
We work from published frameworks, so that findings can be traced to a recognised control and checked by an auditor.
STRIDE, attack trees, MITRE ATT&CK for adversary techniques and MITRE D3FEND for countermeasures.
OWASP Application Security Verification Standard, OWASP Top 10, OWASP API Security Top 10 and the Mobile Application Security Verification Standard.
NIST Cybersecurity Framework 2.0, ISO/IEC 27001:2022 control mapping, IEC 62443 for industrial automation and CIS Benchmarks for system configuration.
Burp Suite, OWASP ZAP and Nmap for testing; Semgrep and Trivy for code and container scanning; Wireshark, Zeek and Suricata for network analysis; Wazuh and the Elastic Stack for log analytics.
TLS 1.3, mutual TLS between services, hardware-backed key storage, and an inventory of public-key algorithms as the first step toward post-quantum migration.
Industry use cases
These are representative applications of this service. They describe the kind of work we take on, not past clients.
Segmentation between office IT and plant OT, controlled remote access for equipment vendors, and passive monitoring of PLC networks.
Protection of patient records, isolation of networked medical devices and audit trails for access to clinical systems.
Security review of customer-facing APIs and network management systems, and log retention aligned with CERT-In requirements.
Security testing of citizen-facing portals before release, and hardening of hosting environments.
Device identity and signed firmware updates for fleets of field sensors and gateways.
Engagement models
Start at the level that matches your certainty about the problem.
An architecture review, a threat model, or a gap assessment against ISO/IEC 27001, the DPDP Act or the CERT-In directions.
A pilot on one segment or one application, such as a detection pipeline or identity-based access, to measure effort and benefit before a wider rollout.
Implementation of controls, a secure development pipeline, or a detection and response stack, with documentation and training.
Scheduled retests, detection rule tuning, patch advisories and incident assistance, with support hours defined in the contract.
Delivery process
Define targets, test windows, rules of engagement and emergency contacts. Testing starts only after written authorisation.
Model the system, then test it with manual techniques supported by automated scanning.
Rank findings by exploitability and impact, assign owners and agree fix dates.
Help implement fixes, then retest each finding and record the evidence.
Tune detections, rehearse response, and repeat the assessment when the system changes.
FAQ
A vulnerability assessment lists known weaknesses across a wide scope, mostly with automated tools. A penetration test goes further on a narrower scope: a tester chains weaknesses together to show what an attacker could actually reach. Most organisations need the first regularly and the second before major releases.
Testing is non-destructive by default and runs in agreed windows. We use a staging environment where one exists. In industrial networks we begin with passive observation, because active scanning can upset older controllers.
No. Certification is issued by accredited certification bodies, and some Indian regulations require audits by CERT-In empanelled auditors. Our role is preparation: gap assessment, control implementation, evidence collection and fixing what an audit finds.
A report with a management summary, each finding with a severity rating, reproduction steps and fix guidance, and a retest of the fixes. Findings can also be delivered as tickets in your issue tracker.
Yes. Models bring their own risks: poisoned training data, model theft through an API, adversarial inputs and prompt injection. We assess these together with the surrounding infrastructure. Our article on securing machine learning models in production describes the approach.
We sign a non-disclosure agreement, collect the minimum evidence needed, store it encrypted, keep credentials out of reports and delete engagement data on the schedule agreed in the contract.
Related services
Models for vision, language, forecasting and anomaly detection, built with reproducible training pipelines and deployed with monitoring.
Explore AI & Machine LearningFeasibility studies, hybrid quantum-classical prototypes and post-quantum cryptography migration, with honest limits on what current hardware can do.
Explore Quantum ComputingOptical wireless links and networks for RF-restricted and security-sensitive indoor spaces, from link budget to installed access points.
Explore Li-Fi SystemsFurther reading: Securing machine learning models in production
Tell us what the system does and what worries you about it. We will propose a scope that fits the risk.