What is Vishing? Tips to Prevent Vishing

Have you received a phone call asking you to share your bank details? Welcome to the world of vishing! Find out here how you can avoid such attacks. We have all received those regulatory government messages at some point; the ones that tell us not to share our personal details over the phone.

 

Why does such a threat exist? It is due to vishing or ‘voice phishing’. Vishing is a scam via which someone can get you to share personal information on a phone call. It is the telephonic variant of phishing. Let us find out why this can be dangerous in the hacker-friendly universe.

 

What is Vishing?

 

According to Bill Murphy’s 2018 statistics, nearly 30% of all incoming calls were scams. 75% of these scam victims also revealed that such vishers already had some personal information about them beforehand.

 

What threat can sharing such information through the phone cause you? Scammers can cleverly socialize with you to get you to share your personal and financial information like OTPs, passwords, account numbers, social security numbers, etc. You would be aware of email phishing attacks. A visher does the same thing over the phone.

An attacker can make their phone number appear legitimate by ID spoofing; a technique that manipulates caller IDs to appear authentic. This is way more viable than email phishing since many such calls can be made simultaneously using VoIP. There are multiple ways they could get you to succumb to their pretentions.

 

They would pretend to be a representative from your bank or the government. A master at his game can easily gain your trust over the phone. With the information you have innocently shared with them, attackers can carry out crimes like identity theft and robberies.

 

How to Prevent Vishing?

 

Technology has surely made lives easy for us.  Alongside such boons are the huge pitfalls that one could fall into. One of such potential risks is vishing. Therefore, it is important to be able to avoid such scams. Here are ways in which you can prevent visher threats by recognizing them.

 

  1. The caller usually claims to represent some governmental, medical or bank organization. If your bank or other governmental agencies have never connected to you via a phone call, this is your first red flag.
  2. More often than not, banks send out messages asking customers to refrain from sharing bank account information or OTPs to strange callers who claim to represent their organization. If you feel doubtful about the caller, ring up the organization they are claiming to represent to be sure.
  3. The caller tries hard to dig up more information from you. If the caller is a representative of your bank or government and they tend to seek your personal data, do not abide to it. No governmental organizations would ask your financial and security information via a call.
  4. Look out for ID spoofing. The Voice over IP technology can convince you that the caller is from a trusted organization. If any such representatives are asking for your information, do not share anything with them.
  5. Once you feel you have been attacked by a visher, report it to authorities. You may either contact the organization they claimed to represent, or go to the police/cybercrime department.
  6. Be cautious over the phone. Especially if official calls are made to you, refrain from seeming too vulnerable. These attackers would be smart talkers. Do not fall prey to them.
  7. Ask them questions. They are bound to break at some point. These fraudsters would not be able to provide the most authentic information about the organization they pretend to represent. This can be used against them to check their legitimacy.

 

In Conclusion

 

Keep an ear out for urgent phone calls that seem in dire need of your personal information. They may tend to make you believe that you are in some sort of trouble. If this seems strange, trust your gut. Many vishers also pretend to be insurance agents and telemarketers.

 

In this world of immense possibilities, it is also plausible to be deceived rather easily. Therefore, it is up to us to stay safe. Be cautious of such fraud calls and keep your information personal.

Product Demand Forecasting in Retail Business

In today’s competitive business world, it is essential to predict how many products/services the customer will buy over a specific period. However, it is one of the hardest analyses to get right. Companies that fail to forecast the demand may face losses across various sales channels. There is a great need for product demand forecasting as it helps in making the right management decisions, predict the budget, launch a new product or scale the business.

 

 

What is demand forecasting in retail?

 

Demand forecasting is an important element in every retail business. It is a lot more than just predicting the demand for your products. It is impossible to have the right amount of stock in place without proper product demand forecasting.

 

 

Demand forecasting helps businesses to manage the cash flow and maintain lean operations. Every business model looks for ways to cut down the cost, as it is one of the easiest ways to maximize profits. You can cut costs in a few ways by implementing product demand forecasting in your business.


Related – How Data Analytics can Increase Sales (POS)?

 

The use of data and insights helps in predicting how much specific products/services the customers want to purchase during a period. In the traditional method, demand forecasting is a bit difficult – as it is seen as a whole.

 

 

There is a wide range of forecast solutions that retailers can make use of today. Some of these methods lead to timely forecast to increase sales and profits. Here are a few cases of demand forecasting –

 

 

  1. Make informed decisions
  2. Anticipate the needs of the customer
  3. Measure the progress of marketing efforts
  4. Streamline production process
  5. Plan the marketing budget across multiple channels
  6. Improve customer experience
  7. Prepare accurate budget and finances

 

 

How to forecast product demand accurately?

 

 

Usually, in the traditional method of product demand forecast – several flaws reduce the accuracy of data. To forecast the demand accurately – retailers may need to use internal and external data metrics such as – competitors, consumer trends, historic sales numbers, website traffic, ad spend and more.

 

 

Some of the best techniques to forecast demand include –

 

  1. Qualitative Analysis

In this technique, the business anticipates the product demand based on qualitative data. This data is based on factors like – expert opinion, market research, surveys, focus groups etc rather than facts.

  1. Causal Model

The causal model accounts for forecasting based on controllable (marketing, price, sales, location etc) and uncontrollable factors (weather, politics, competitors, seasonality etc)

  1. Time Series Analysis

This is a quantitative approach for forecasting the demand. It is based on exact facts and figures, trend analysis, graphical methods, and life cycle modelling etc rather than soft inputs.

 

 

Wrapping up!

 

The importance of product demand forecast is very high for the retail business. Organizations can reduce the risk and make informed decisions by making an accurate forecast of product demand.

 

 

If you are new to forecasting the demand, you should establish a baseline for data. The simplest way is to pull in the sales from previous years and plan your sales by day. It is also important to understand the customer and their shopping techniques with predictive data analysis.

 

 

Looking for a product demand forecasting solution? Get in touch with us at [email protected]

 

How to improve inventory planning?

Since globalization, organizations are facing challenges in managing the inventory. With the supply chain becoming complex these days, inventory management has become the need of the hour. Companies often experience challenges in gaining valuable insights from the data that help them to forecast demand and inventory planning.

 

Data Analytics powered by cutting edge technologies like machine learning and artificial intelligence can improve the accuracy of inventory orders. It also increases the work efficiency and productivity by eliminating manual processes. The lack of planning and organizing the inventory can upset the customers and may hamper productivity.

 

There are several factors like weather, holidays, economy etc that can change the demand and supply pattern. Before data analytics, enterprises often used traditional inventory management methods like – excel spreadsheet to figure out the customer demands and supply chain.

 

However, as the needs and expectations often fluctuate, it became difficult for retailers to maintain the accuracy of data and make business decisions.

 

In most of the enterprise scenario, inventory planning and management through data analytics can help companies improve their operations and make more intelligent business decisions.

 

Benefits of Data Analytics in Inventory Planning

 

Big data analytics processes a large volume of data. However, the high volume of data without proper infrastructure can create more problems. Here is how big data is changing inventory planning and management –

 

 

 

 

  • Improve efficiency

With Big Data Analytics, enterprises will have better access to the metrics and have an overview of real-time data. This helps in removing the bottlenecks and enhances the performance proactively compared to traditional methods of inventory planning.

 

 

  • Increase customer service

Having access to real-time data will help the enterprise to understand customer preferences and predict seasonal trends. The spikes in the customer requirements can be measured accurately to ensure that the inventory is maintained all the times.

 

 

 

 

 

  • Maximize sales

The biggest advantage of data analytics is to understand the customer purchasing trends, best/worst products inline, best/worst performing sales channels etc. Enable successful sales across multiple channels and markets by getting visibility into the inventory.


Related – How Data Analytics can Increase Sales (POS)

 

 

 

Bottom Line

 

 

Big Data Analytics can transform inventory management capabilities, improve operational efficiency, reduce costs, maximize sales and reduce inventory shrinking. The application of data analytics for inventory planning has a positive impact on profitability.

 

The key to data analytics in inventory planning is to have the right product quantity at the right time and right place.

 

If you are looking for a specific solution for inventory management, it is recommended to check the existing systems and process of implementation.

 

Get in touch with us at [email protected] to know more about a detailed inventory management system.

How Data Analytics Can Help Increase Sales (POS)

What is POS data? Can you make use of this data to improve your business and marketing strategies? Find out how data analytics can help increase sales (POS)

 

We have all made payments through the POS machine at some point. If you are a retailer or a business owner, there is more to that interface that you should know about.

 

 

Read on to find out how POS can transform your business and help increase sales. POS or Point of Sale or Point of Purchase is the place where sales are made, whether it is a shop or marketplace. Retailers consider POS to be the place in which a customer completes a transaction, such as a checkout counter.

 

 

 

The improvements made in technology have made this economy a cashless one in many ways. A POS machine is an instance of the same. POS options allow retailers to access customer data that they can use for the betterment of their business. Many marketers are unaware of this facility.

 

 

Retail Data Analytics

 

Everything that is involved in a financial transaction or sales can be accessed using POS. Inventory, customer data, their other purchases and even their foot traffic in your shop can be traced using this data.

 

 

Surprised?

 

 

What can you do with POS data?

 

 

There is immense competition in each market. Merchants are fighting among themselves to provide the best possible customer experience, both online and offline. POS data can offer you an edge over your competitors.

 

 

POS data can help you track your customers’ online behaviors to match up to their requirements offline. That is, you can do away with guessing your customers’ preferences. With the heap loads of information you have at hand, you can provide them with exactly what they seek.

 

 

You may use this data for planning and renovating your business, updating your inventory, marketing and for the management of employees.

 

 

 

The Advantages of Data Analytics

 

 

The switch from manual data analysis to cloud-based systems has shown small marketers a 400% boom in their production and distribution stats.

 

 

POS software can utilize retail analytics forums to build novel strategies for your business. Small business owners do not necessarily need an advanced POS system to access useful customer data. If you are thinking about choosing a POS system, pick one that is most suited for your industry.

 

 

Following POS systems are a few of your options.

 

 

 

How to Collect POS Data?

 

Data collection from a POS system can occur in two levels; passively or actively.

 

 

Passive data collection helps a retailer predict user preferences based on the history of the customer’s association with the firm. Information about most visited sites, times of purchase, other website usages etc. too follow the same passive strategy.

 

 

Active data collection can be done via a robust POS system when a customer purchases from your website. Registration forms that are part of the purchase process are an example of the same.

 

 

Active data collection lets you access more data than the former strategy. But with more information comes bigger responsibilities.

 

 

You need to ensure the complete safety of customer data since it might carry sensitive content. One error and the business can go downhill easily.

 

 

Is Data Analytics Right for your Business?

 

 

The impact data analytics can have on your business completely depends on your industry and customer demographics.

 

 

Nearly 67% of small marketers don’t make use of this technology. Therefore, there is a competitive advantage that this data can offer you. If your competitors have already begun to use data to increase sales, you need not hesitate.

 

 

What is the Role of AI in Cyber security?

What can Artificial Intelligence do to keep your database secure? Is AI advisable in the cyber security industry? Find out the pros & cons of AI in cyber security.

Storage of data has become increasingly easy in the past few decades with the advent of technology. We don’t need huge storage rooms to stack heaps of papers anymore. Everything can be stored in soft forms in a database of a computer. With this advantage also rose the risk of data breaching or cyber-attacks.

 

Technology keeps advancing towards betterment with each on-going second and so do novel methods of attacks. The security-attack duo seems to be in a constant loop trying to outdo each other.

 

An attack on your precious data can lead to the leakage of important information including credit card numbers, social security numbers, passwords and other personal information.  So where lays the solution? The solution is Artificial Intelligence!!

 

Role of AI in cyber security

 

Smartphones and other gadgets have already begun incorporating bio-metric systems of security into their mechanism. Fingerprints and face recognition features are all part of this.

 

 

Do this AI can remedy the potential risk involved in saving data in databases? Let’s find out.

 

According to the report on the relevance of AI in cyber security, two out of three firms are planning to adopt AI solutions for security purposes in 2020. AI is the future of the cyber security industry. It promises the identification of threats for better results and stronger security.

 

  1. Bio-metric logins are much more secure than password-protected systems.
  2. Artificial Intelligence can detect threats and malicious activities much faster than other security strategies. With heaps of data to be scrutinized for potential malware, the task could get rather troublesome.
  3. AI can reduce the routine responsibilities of people and minimize human labor.
  4. AI runs pattern recognition in software to detect even the slightest behavioral changes.
  5. This technology can predict potential risks. According to the IEEE Computer Society, AI can detect threats at 95% better rates than traditional methods.
  6. AI utilizes natural language deduction to process information to identify threats.
  7. An AI-powered authentication strategy is more dynamic and advantageous to users.
  8. Multi-factor authentication helps the technology to understand a user’s information to deduce their behavior and determine access privileges.
  9. The recognition of cyber vulnerabilities becomes easier with AI. An imitation algorithm can pose high risks at data breach.

 

 

Bottom Line

 

There are high chances that cyber criminals have beaten you to AI-powered algorithms. However, with the increase in cyber threats, keeping faith in the advantages posed by Artificial Intelligence is an option many firms are conforming to.

 

 

The most important thing to be looked after while asserting an artificially intelligent cyber security system is that constant updates are inevitable. A good technician would know to keep up with the race and ritualistically update algorithmic weaknesses from time to time.

 

 

AI is not a substitute for human security systems or engineers. However, it can surely facilitate another level of security which wasn’t hitherto possible. Faster detection of risks prioritizes cyber defense systems above human ones.

 

Recommended Posts –

 

  1. What is DDoS attack? Tips to prevent DDoS attack
  2. Data Protection and Privacy Laws in India

What is the Future of Cyber Security in India?

Do you know the future of cyber security in India will have a heavy focus on using AI.  Well, the automated systems are likely monitor, prevent and manage cyber attack in real time.

 

 

Indian companies are seeing a significant increase in the number of challenges they face in cyber security. Amid the shift to mass remote working, organizations have experienced a huge shift in cyber threats. According to sources, the majority of the reputed organizations in India were unprepared for the change. Only a few organizations adopted cyber security measures to support the remote working environment.

 

How the future of cyber security in India will look like?

 

With the growth of cyber crimes and data breaches, there is a requirement of new technologies. Since we live in an evolving world that is fueled up with advanced technologies like – Big Data, AI, ML, etc – there is a war between defenders and attackers. Companies need to migrate from traditional methods to protect their IT infrastructure from cyber attacks.

According to the report developed by Data Security Council of India, over half billion people in India are using internet. With the increase in number of internet users, cyber criminals have shifted their focus to India. There is a greater focus on pushing organizations into adopting minimal security  standards.

No doubt, emerging technologies are the future of cyber security in India. Here is how the advanced technologies and tools change the future of cyber security –

  • Attacks would be cheap and lucrative

Attacks would become cheaper and lucrative with bots doing most of the activities in finding vulnerabilities. According to a study, 30% of Indian companies will be victims of a cyber attack in the future. It security managers believe that their current defense systems are not sufficient enough to block the future cyber attacks.

 

 

 

  • Automated networks

Automated networks are likely to reduce the risk of cyber attacks. With automated scanning and monitoring of connected networks, one can report the animosities and deviations in real-time. The automatic updating of firewalls, endpoints, network, payloads and anti-viruses with the help of AI/ML can be a game-changer in preventing cyber attacks. In such a scenario, companies should look for advanced cyber security tools to prevent their digital assets from potential attack.

 

 

 

  • Block chain wave

Block chain is one of the popular cyber security technologies that are likely to grow tremendously in the coming years. The company can leverage a key infrastructure to authenticate the devices with the help of AI and keep the potential attacks at the bay. Organizations are likely to face a lot of phishing scams and hence they need to adopt smart cyber security techniques.

 

 

 

 

Bottom Line

 

 

In the future, companies are likely to create integrated capabilities to address and prevent cyber threats and reduce damage from cyber attacks by using a well-defined governance framework. Companies will need a comprehensive and unified IT security provider for creating cyber-defense network.

 

 

Looking at the digital trends, we see the cyber security as a growing concern in the coming years. Companies equip with better security tools and technologies to prevent major data breaches and cyber attacks.

 

 

If you have concerns about the IT security of your organization, get in touch with us at [email protected].

Types of Data Analytics to Improve Decision Making in NBFC

Wondering where and how to implement different types of data analytics? Well, Data Analytics is going to play an important role in the banking and non-banking financial sectors. With Non-banking financial companies believe that the core information can be ascertained by analysing the digital footprint of a person. The focus on big data has become so profound that NBFCs are giving more importance to the data than the CIBIL score.

 

 

Types of data analytics

 

 

 

Data Analytics provide better insights into the individual’s behavior. There may be cases where the person may default the loan due to unavoidable circumstances, even if he is genuine. The objective to use data analytics in financial decisions is to understand the behavior of the individual using different types of data analytics.

 

 

 

NBFCs must understand customer behavior to shape their products and services. Here is how the four types of data analytics help in the decision-making process.

 

 

 

 

  1. Descriptive Analytics

Using this technique, one can gather the past data of customer from multiple sources. The gathered data is combined with other data analytics strategies to get insights into customer behaviour. It offers a comprehensive way of viewing the key metrics within the organization.

 

Also Read – How Data Analytics can help NBFCs grow?

 

Additional, it compares the real-time data with the historical data to derive meaningful insights. In short, the data collected for Descriptive Analytics is used to identify the reasons behind success or failure of an action/event in the past.

 

  1. Predictive Analytics

This type involves analysing the past data trends and patterns to forecast the outcome accurately. It helps in identifying clusters, exceptions and tendencies – while giving insights into future trends. NBFCs can determine the realistic goals and its effective execution by manipulating the findings of descriptive and diagnostic analysis.

 

 

 

  1. Prescriptive Analytics

This method helps financial companies to understand the course of action based on the data gathered. In this type of data analytics, financial companies can understand the possible reasons behind all the complications and challenges – and work on the best course of action.

 

 

Prescriptive Analytics is applied when financial companies have an impact on the overall operations.

 

 

  1. Diagnostic Data Analytics

As the name suggests, this data analytics is all about breaking down the data and identifying the reasons behind specific events, behaviors and problems. With this type of Analytics, the managers can search and create snapshots of customers in a particular branch.

 

 

They can also compare the credit score, loan amount, term and succession metrics to get into the root cause of any problem. Some of the popular techniques used in this type of data analytics include – data discovery, drill down, data mining and correlation.

 

 

Wrapping up!

 

 

Data Analytics can improve the way financial companies attract, retain and grow customers. NBFCs are deploying analytics to lower cost, improve efficiencies and increase profitability to support risk and regulatory compliance priorities. With data analytics, financial companies can –

 

 

  1. Design and market better products based on what people need
  2. Assess the risk profiles of their customers in detail and make better decision
  3. Develop new business models & sources of income
  4. Improve customer service by analysing data in real-time

 

Amvion Labs help banking and non-banking financial institutions to analyse their data through data visualization and other data analytic strategies, enabling them to extract valuable insights for decision making.

 

Get in touch with us to know more about our services.

How NBFCs can resolve issues of credit availability through AI and Analytics?

Non-Banking Financial Companies (NBFCs) have emerged as a reliable alternative to the mainstream banks in India. Thanks to the rise of technology that allowed NBFCs to make use of AI, ML and Analytics to build products for their customers. Today, several NBFCs are using big data technologies to resolve issues of on – loan management, fraud detection, regulatory compliance and credit availability.

 

How AI-Powered Analytics Analyze Credit Availability?

 

Customers are the vital focus area for NBFCs. Hence, they are focused on developing innovative products, catering to low-income people in unorganized sectors. In such a scenario, companies should adopt AI powered business models and Analytics to facilitate the design and launch of customized products. Here is how NBFCs can resolve issues of credit availability and loan management by using AI and Analytics –

 

 

  1. Credit Risk Assessment

Although India’s NBFCs are growing their market share, they need to be careful about money handling and credit risk assessment. The traditional method of credit risk analysis is based on the past experiences with the customer.

 

 

 

 

 

On the contrary, analyses the credit risk using a data-based credit scoring system. With millions of Indians holding loans worth trillions of rupees – the big data technologies like AI and Analytics can help NBFCs improve the returns on the loans they hold.

 

 

  1. Alter Status Quo

The issue of credit availability has garnered a global concern in recent years. Data Analytics provides NBFCs with an opportunity to transform the way they allocate credit and risk. The ability of AI and ML technologies to avoid traditional credit reporting system allows NBFCs to alter the status quo. The goal of NBFCs in this regard is to incorporate new data and provide credit to customers on better terms than the current.

 

 

  1. Fraud Detection

Today, NBFCs face an overabundance of poor quality credit. Additionally, the use of too many sources of data is causing an alarming situation. As per the law, it is illegal to discriminate gender while determining credit eligibility. However, there are countless proxies for gender, which makes the data redundant and inaccurate. NBFCs can make use of AI & Analytics to get a clear picture of the proxies for race and increase data accuracy.

 

 

  1. Credit Score

The use of AI, ML and Analytics to analyse data in credit availability and loan management is likely to grow significantly. There are billions of people without a real credit history. It is obvious that the more data you collect about them, the more likely you would be to predict/understand their behaviour – including their chances of repaying the loan.

 

Conclusion

AI is the way of future and implementing in financial sectors may potentially increase the country’s economy. Banks and NBFCs in India should adopt AL and ML to stay relevant in the ever-changing financial environment.

 

 

Data Analytics can accurately use the digital footprint of the individual to determine their creditworthiness. Eventually, it  can yield better results than traditional methods. Instead of finding people with a low credit score, NBFCs must use Analytics to give plausible excuses to provide credit to those with a poor score.

 

 

In short, Big Data technologies have created an excellent opportunity for NBFCs to provide services without taking additional risks.

 

 

Get in touch with us at [email protected] to know more about the benefits of Data Analytics.

 

Recommended Posts –

 

  1. How data analytics can help NBFCs grow?
  2. Types of data analytics to improve decision making in NBFC

 

4 Best Practices to Integrate Security into DevOps

Today, companies spend a lot of time and efforts on improving security. In the ever-growing CI/CD, the developer team missed out from going through all the necessary checks. Organizations can reduce the chances of data breaches by making security an integral part of DevOps.

 

 

 

Implementing security measures should be a top priority to ensure success in the application development life cycle. The application layer is one of those areas where potential damage can occur. Because of the security issues, confidential information can be exposed – resulting in damage to the company’s reputation.

 

 

 

Traditionally, the DevOps team is focused more on developing the application and delivering on time with little consideration on security. This can be because of –

 

 

  • Lack of security awareness
  • Keep adding new features to meet business requirement
  • Inadequate knowledge of security solutions

 

 

To address these issues and bring security into the SDLC or ADLC, there is a “shift left” approach. This approach, also known as ‘DevSecOps’ helps in securing the software/application throughout the lifecycle.

 

What is DevSecOps?

 

 

DevSecOps is the new requirement of many industries, which is based on the principle of Development + Security + Operations. Adding Security to DevOps ensures that all the teams – be it developers, operations, security and project managers collaborate and work together from the initial stage of the development process. This will help developers to code the products/applications securely – eventually increasing security of DevOps.

Why Should You Move to DevSecOps?

 

A DevSecOps approach is beneficial for the internal/external team and the customer, as security is integrated into the product from day one. Organizations adopt DevSecOps when they seek –

 

 

1.       An advanced alternative

By integrating security at the initial stage, the issues are identified and resolved faster – resulting highly secured software.

 

 

2.      Faster and cost-effective delivery of the product

DevSecOps approach enables the teams to release the updates beyond the initial launch at a faster pace.

 

 

3.      Transparent workflow

DevSecOps addresses different aspects of development and delivery process, ensuring transparent workflow.

 

 

4.       Faster recovery from threats

The DevSecOps approach ensures a strong collaboration between the development and security team – which results in faster detection and remediation of vulnerabilities.

 

 

Best Practices to Integrate Security into DevSecOps

 

 

DevSecOps offers immense benefits such as tackling security issues, easy remediation of vulnerabilities, controlling the risk and more. Here are the best practices to add security into the development process.

 

 

1.      Security Automation

 

The foremost requirement of CI/CD is quick delivery. While producing the code quickly in agile sprints, developers may not pay much attention to manual testing. Automating the security testing will provide comprehensive vulnerability coverage without compromising the code.

 

 

Some of the popular security automation tools are – CodeAI, Parasoft tool suite, RedHat Ansible, StackStorm, Veracode etc. The majority of security automation tools identify 14% of the vulnerabilities. Hence, it is vital to use multiple tools for comprehensive coverage. While the automation tools take care of the security, your team can take care of the required fixes without slowing down the development process.

 

 

2.      Reduce Code Dependency

 

Organizations often use open-source software – despite the growing concerns of security issues in third-party software. Developers often lack time to read the documentation or review code in an open source library. If the open-source usage is causing vulnerabilities in the code, it may harm the dependant code.

 

 

Code dependence checks can ensure that developers do not use code with known vulnerabilities. The OWASP Dependency-Check is one such tool that checks third party components for vulnerabilities.

 

 

3.      Threat Modelling

 

Threat modelling practice gives a better idea of threats to your digital assets. It helps in identifying and prioritizing threats in the application and helps mitigate them. However, it can be challenging as it may reduce the speed of CI/CD process. This approach helps the developers see from the point of view of an attacker and encourage more communication between the security and developer team.

 

 

Some of the popular threat modelling tools include – IruisRisk, ThreatModeler, and OWASP Threat Dragon. These tools automatically build threat models and help the security team to explore threats and their impacts.

 

 

4.      Use DevSecOps tools

 

Organizations can use DevSecOps tools to integrate security into DevOps. These tools can manage security across the entire CI/CD pipeline. Some of the popular tools are – Aqua Security, GitLab, Dome9 Arc, Red Hat OpenShift and RedLock. These security tools help developers to initiate scans quickly and get results without being interrupted.

 

 

Integrating the DevSecOps tools into the building process helps check the security and licensing of multiple components to keep the apps secure in production.

 

 

 

Wrapping Up!

 

 

To conclude, DevSecOps is an excellent way to add security to DevOps culture from the initial stage. I hope this post helped you transition into DevOps culture while integrating security process.

 

 

Improving security in a few areas of application development can help eliminate vulnerabilities in the initial stages, saving a lot of time and cost for the organization.

 

 

You can determine the risk tolerance in the context of the organization by engaging in-house security teams or outsourcing to a reliable DevOps service and solution provider.

 

 

If you are struggling to integrate security into your DevOps, get in touch us at [email protected]. We will provide a tailor-made solution, based on your business requirement.

 

 

Recommended Posts –

 

How Data Analytics can help NBFCs Grow?

Data is now more accessible than before and plays an important role in improving the efficiency of business processes. Data analytics is a valuable way for NBFCs to refine their marketing process and improve business management. It tells about the health of the organization and whether you are on the right path to achieve your business goals or not.

Whether it is increasing the customer base or servicing existing customers, data can be used by NBFCs in many ways. Today, companies are widely embracing data analytics to streamline operations and improve work processes.

Use of Data Analytics in NBFC

Customers are vital for every business. Hence, NBFCs all over are focusing on developing innovative products to cater to customers of all kinds. Investing in data analytics can help NBFCs to lower their cost while overcoming the credit penetration issues in a growing economy. Here is how Data Analytics can help NBFCs –

1. Introduce customized services and products

To reach the target audience, NBFCs need to work constantly on a host of strategies to bring out a wide range of customized and innovative products. By analyzing the data, NBFCs would allow customers to transact the way they want, rather than following a standard set of protocols.

2. Expand customer base

Implementing big data analytics will help financial institutions to reach customers who are difficult to reach through traditional methods. Incorporating big data tools will help NBFCs to focus more on rural business and handle various loan processes instantly without any hassle. Moreover, it is a cost-effective way to bring new customers – as data analytics can bridge the gap between customers and financial institutions.

3. Reduce operational cost

Data Analytics make operations much easier for NBFCs. It allows proper utilization of digital and traditional assets without the need for additional resources. The biggest challenge faced by NBFCs in this regard is the higher cost. With the introduction of SaaS and Cloud-based models, the Analytics solutions are available on-demand at a much lesser price.

4. Improved customer service

The best way to serve the customer is through email updates and text messages. Digitization will help NBFCs to engage their customers and build a personal relationship. A satisfied customer is likely to offer references and word of mouth advertising – which is the best way to bring in more business. Big Data Analytics give NBFCs to an opportunity to understand their target audience and expand further to reach their goals.

Wrapping Up!

Typically, NBFCs have a small IT team and have to deal with tons of data. NBFCs may face challenges when there is a change in the data infrastructure or data migration. The array of data visualization tools can aid in setting up a cost-effective analytics platform that will prove decisive in the growth of NBFC organizations.

With multiple use cases, ML and AI would be a huge leap in data analytics. Starting from customer feedback to credit risk modelling, AI-based Analytics model can prove convenient for loan disbursements, PAR Reports, Loan Collections and more.

If you are looking for an extremely agile and tailor-made analytics solution, get in touch with us.