How to perform a SaaS Security audit?

October 20, 2020

Software as a service (SaaS) is becoming quite popular in several industries and so is the SaaS security audit, including banking and other financial institutions.

An integrated SaaS platform should be easily accessible and secure. When you store data off-premises, you need to be sure that it is safe from attackers and third parties. Any breach in the system can potentially disrupt the workflow and put all data at risk. Before migrating to a SaaS system you need to be sure of how secure and resilient the platform is. Also, regular security audits are required to detect and fix new security vulnerabilities. Constant monitoring and regulation are required to keep the SaaS platform safe and operational.

A SaaS security audit should capture everything from security habits to different security protocols. It should be able to find out any vulnerability present and fix them. This guide will help you conduct a complete security audit of your SaaS platform.

Steps for an effective SaaS security audit

Data Management and Governance

Thoroughly review organizational strategies and appetite to risk, roles, and responsibilities of employees, and different tasks related to governance. Users should be able to monitor the usage of SaaS platforms through dashboard from vendors and logs captured by users.

  • Have a trial run of data flow and review privacy policies throughout the data’s life cycle. If it is vulnerable at any point then apply necessary fixes to plug the gap.
  • Check encryption and security during data transfer to and from SaaS platforms.
  • Data segregation is important as sharing data increases the chances of it being vulnerable. Review the sharing of environments and security permissions for various stakeholders. If required, you can opt for separate servers for different categories of data to ensure proper partitions.
  • An important part of the SaaS security audit is data backup. Make sure that your platform can take regular backups of data and quick restoration in events of disaster. Backups should be securely stored and easily accessible too.
saas security guide

Image: Back up architecture (

Infrastructure review

A SaaS security audit should also consider the infrastructure used by the service provider. There are various aspects to accurately measure how robust the infrastructure is.

  • The audit should be checking if any user access is monitored and restricted to all assets of the system. They should have a strong and secure network to efficiently manage all traffic and interconnections between services.
  • There should be regular security updates and systems should receive security patches for any known or discovered bugs. Virtual machines should be regularly updated and securely connected to all services.
  • Review the control and storage of encryption keys. Test encryption certificates and their storage locations too.
  • Firewalls are necessary to stop attacks from reaching the servers and protect them from unauthorized access. Your SaaS platform should have a strong firewall with features such as intrusion detection, anti-malware, DDoS attacks, etc.
  • Regular penetration testing will help you discover any security flaws or gaps. SaaS providers should have periodic penetration testing on the system.

Logs and auditing data

Logs are very crucial and both the service provider as well as users should have automatic log capture and storage. These are important during a forensic investigation and system analysis and helps in quickly resolving issues. The SaaS security audit should also check for mechanisms to prevent tampering of logs and proper storage.

Availability and access to data

The SaaS security audit should be able to determine the quality of storage and interconnections. Also, analyzing the uptime of the services is an important factor.

  • Take note of factors such as cluster systems, failover capabilities, and redundancy. These measures prevent the systems to completely fail and ensure that it is quickly back online.
  • The storage location of backups plays an important role as faster access to backups will help you restore systems in case of a failure.
  • Also, check if they have a robust plan for handling incidents. Enquire your SaaS provider is capable of handling traffic at peak demand by putting the system under stress tests.

Privacy issues

Privacy concern is an ever-growing one and a SaaS security audit should check if your data is completely private.

  • Enquire regarding the storage of client data and how they dispose the data.
  • You will also need to know if third parties can access your data and under which conditions. And in case if resources and logs are accessible by third parties, it should not reveal your sensitive information.

Regulatory compliance

To gauge the quality of your SaaS provider review their cybersecurity certificates and accreditations. There are a few top accreditations for SaaS companies:

1. ISO 27001: ISO 27001 is an international standard that is relevant to SaaS providers and is considered as the gold standard for security

2. SOC 2: It is a well-respected security auditing framework that indicates a very high level of security standard for SaaS platforms

Image: SOC framework (imperva)

3. OWASP ASVS: This is an open and standardized framework for SaaS providers to test and harden their security systems

4. CSA STAR: This is a relatively new attestation and many consider it to be the future standard for cloud assurance and trust. Leading cloud platforms are getting certified as CSA STAR

saas security audit

Image: CSA STAR Accreditation framework by BIS

Security is not the end-point but a continuous journey of improvement and augmentation. Cybersecurity researchers are discovering newer threats and service providers are continuously working towards fixing them. That is why regular security audits are necessary to find any security gaps or vulnerabilities. SaaS security audit by Astra Security can find any security flaw that is present in your system and help you plug them. Astra does more than 300 security tests and has dashboards to view complete audit details. With Astra, you can be sure that you are safe from cyber-attacks.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

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This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

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