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Are Online Payments Safe In The Current Digital Age?

This makes ACH payments secure than other forms of compensation. Although ACH payments are designed to be safe, you can further improve their security. Some of the ways you can do this include:

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CYBER SECURITY Business technology Antivirus Alert Protection Security and Cyber Security Firewall Cybersecurity and information technology

It’s the worry of every business owner to be involved in any form of fraud and lose their hard-earned money, and it’s even worse when they even implicate their loyal customers.

If it makes sense to collect ACH payments for your business, your biggest question is, are ACH payments safe, right?

Well, yes, they are safe. ACH is regulated by the federal government and operated by the National Clearing House Association (NACHA), an NGO that enforces rules and regulations while keeping the account numbers confidential.

This makes ACH payments secure than other forms of compensation.

Although ACH payments are designed to be safe, you can further improve their security. Some of the ways you can do this include:

1. Work with a trusted payment processor.

The payment processor has a significant impact on the safety of the transactions, so you need to be cautious of the one you work with. Click here for a reputable payment processor if you don’t know where to begin.

An ideal processor should be compliant with the NACHA’s operating rules and seal all risky areas where you can lose money. For example, to avoid losing money when a customer with insufficient funds makes payment only for it to be rejected, work with a merchant with “balance check” tools that confirm that the customer has sufficient funds in their account to cover the said transaction.

The merchant should also have fraud preventing tools protecting you from any form of fraud.

2. Remove human data input.

Even your employees are a risk as they can collect your customer’s information and use it to access funds, instead of emailing your customers or collecting their information over the phone, set up a secure portal where they enter their banking information so that none of your employees can see it.

Security is a major business objective

You also should make use of tokenization and encryption. Encryption is a process of encoding data to ensure that it’s only visible to the intended recipient.

Tokenization replaces the private information with a unique and unrelated set of characters that don’t make sense and are of no value to those seeing it. This way, you don’t worry even if sensitive data lands in the wrong hands.

3. Use two-factor authentication

Doing this deters any bad actors that might maliciously use your platform or service. When you implement two-factor authentication (2FA), you have peace of mind knowing that your money and business are protected so that even if your customers are using weak passwords, no one can access their account without sending the email or phone verification code.

4. Use micro-deposits

Most third-party payment processors make two micro-deposits into a user’s bank account to verify their identity before making a formal financial transaction.

Check with your payment processor and if this option is in the settings, activate it, and it will come in handy at preventing fraud and knowing your customers more as you have all of their details.

5. ACH is safe

As you have seen, ACH payments are safe, and there is no reason you shouldn’t accept them in your business. For peace of mind that even your customers are protected when using your platform, work with a reputable payment processor with all the safety features in place.

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Cybersecurity

The Role of Machine Learning in Cybersecurity

In this article, let’s explore the role of machine learning cybersecurity and the future that machine learning makes possible.

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The Role of Machine Learning in Cybersecurity

Today, the term “machine learning” (ML) is widely used in almost all IT-related fields.  Additionally, machine learning (ML) has proven invaluable in a variety of areas, including cybersecurity. It is routinely used to make sense of massive data, enhance company performance and procedures, and aid in prediction. In this article, let’s explore the role of machine learning cybersecurity and the future that machine learning makes possible.

1. Why Use ML in Cybersecurity?

Cybersecurity can use machine learning to study patterns and help stop similar assaults and react to altering activities. It can assist cybersecurity teams in being more active in thwarting threats and quickly responding to ongoing attacks. It can also shorten the time spent on repetitive work and make it possible for enterprises to employ their assets more wisely.

Scaling up security measures, identifying unknown threats, and identifying sophisticated attacks, such as polymorphic malware, are some of the growing lists of cybersecurity difficulties that can only be solved by machine learning. It can be exceedingly challenging to identify such sophisticated attacks when utilizing a standard signature-based technique because advanced malware can alter forms to elude detection.

Machine learning may greatly improve cybersecurity by making it less complicated, more proactive, and less expensive. However, it can only carry out such tasks if the machine learning is supported by data that fully captures the environment.

CYBER SECURITY Business technology Antivirus Alert Protection Security and Cyber Security Firewall Cybersecurity and information technology

2. How Machine Learning Will Improve Cybersecurity

Current cybersecurity solutions are supported by machine learning in a variety of ways. Each way is valuable on its own, but taken as a whole, they change the game when it comes to keeping a solid security position in the shifting cyber environment. Here’s a list of ways ML will improve cybersecurity:

a. Identification and Reporting

It’s difficult for large companies to be alert of every device that connects to their networks because there are so many of them. Network devices can be identified and profiled using machine learning. The various behaviors and features of a particular device can be determined by that profile.

b. Automated Threat Detection

An advantageous use in cybersecurity is using ML to quickly identify known harmful activities. ML can distinguish between normal and abnormal behavior after initially identifying devices and learning about everyday activities.

c. Scaled Insights

Since data and applications are spread across numerous places, it is just not humanly possible to detect trends across a large number of devices. ML can automate large-scale insights in ways that humans cannot.

d. Policy Recommendations

Creating building security rules is frequently a labor-intensive procedure with many difficulties. Machine learning can support policy recommendations for security devices, including firewalls, by identifying the devices that are present and what is typical behavior.

ML can generate particular suggestions that operate automatically rather than requiring users to manually travel through competing control lists for various devices and networks.

3. Endnote

Only ML can classify complex events and situations to enable enterprises to address cybersecurity challenges now and in the future. This is because more devices and dangers are coming online every day, while human security resources are in short supply.

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