Month: November 2021

Cybersecurity has emerged as one of the most critical concerns that businesses must address as consumers and huge companies are unavoidably vulnerable to cybersecurity risks. The attacker techniques are evolving and becoming more elusive day by day. In addition, since the pandemic, firms that have moved to remote work have grown more exposed to hacking assaults.

With the scale and frequency of data breaches escalating, it’s critical to be aware of the emerging trends in cyber security to watch in 2022.

1. Ransomware assaults

The ransomware attacks worldwide grew by 151% in 2021, making it the top cybersecurity trend of the year. Ransomware is malware that encrypts files once it has gained access to a company’s network. It renders the data useless and the systems that rely on them inoperable, allowing the attackers to demand a ransom in return for their decryption.

One of the recent attacks was the Fantom ransomware technique. This technique of operation was an alarming hacking approach concerning the Windows operating system. Fantom presented its victims with a Windows update-like screen, a virus that encrypted files in the background.

2. Data Breaches

Data is an irresistible force in today’s digital age, and personal data security is essential for total peace of mind, whether for an individual or a business. But, unfortunately, we live in a world where any tiny fault or technical problem in your system might allow hackers to get access to potentially sensitive information, leaving us subject to cyberattacks and identity theft. This issue has been on the radar for a long time, and it will continue to be a concern as time goes on, requiring businesses to address it.

3. Cloud Security

More and more organizations are moving to the cloud with the help of top cloud management software solutions. Most cloud services, however, do not yet provide safe encryption, authentication, or auditing logging. Due to a lack of cloud security settings, fraudsters may override internal restrictions that safeguard critical data in the cloud database. As a result, IT security experts see the need to strengthen cloud security.

4. Software Packing

Software packing compresses or encrypts an executable file and modifies the file signature to evade signature-based detection. Therefore, wherever feasible, defenders should try to narrow out their assault surface. Preventing and detecting these sorts of assaults is easier with an endpoint protection platform (EPP) that records and analyses data over time.

5. Defensive Evasion

The technique adopted by attackers to avoid detection during a breach is known as defence evasion.Uninstalling/disabling security software or encrypting data and scripts are examples of defence evasion techniques. Additionally, attackers may use operating system features to hide ordinarily visible windows from users rather than alerting them to adversary activity. Once again, attackers have demonstrated their willingness to employ system tools and procedures typically used for system management.

If 2020 and 2021 have shown us anything, attackers will continue to alter their tactics, forcing defenders to adapt as well. Attackers have honed their skills at eluding security measures. Their level of quality assurance has improved, and when it comes to command and control, they’ve become more stealthy. As a result, businesses are forced to defend themselves against these attacks, which leaders can only do by upgrading their cybersecurity.cloud security.

The days of rule-based engines and simple prediction algorithms detecting most fraud attempts are long gone. As technology is advancing, the risk of being a victim of fraud is also growing. Now fraud-based assaults have a distinct pattern, sequence, and structure that makes them impossible to identify using only rules-based reasoning and predictive models.

This rapid development of complex, highly advanced fraud efforts can be countered only by AI. Artificial intelligence in cyber security can review years of transaction data in minutes to compute risk scores. As a result, AI is extremely useful in identifying fraud and other financial crime-related suspicious actions.

Here are five ways in which artificial intelligence is driving a new age of fraud detection.

1.  Analyze data with precision

One of the essential characteristics of machine learning is its ability to assess large volumes of transaction data in real-time and identify questionable transactions with exact risk scores. This risk-based analytics technique finds complicated patterns difficult for analysts to spot, allowing banks and financial institutions to run more efficiently while identifying more fraud. The algorithms analyze various elements to fully depict each transaction, including the customer’s location, the device utilized, and other contextual data points.

2.  Detecting fraud in real-time

Rather than needing to wait six or eight weeks for fraudulent charges, AI allows fraud attacks to be detected in real-time. The potential of modern security software to detect fraud assaults in less than a second is the future of fraud control. When a digital organization depends solely on structured learning and rules, new assaults are challenging to detect. AI eliminates the need to constantly play catch-up to online fraud by balancing supervised and unsupervised learning.

3.  Better insights for fraud analysts

With the increasing number of new cyber-threats and massive volumes of data to evaluate, fraud analysts are faced with the near-impossible challenge of quickly recognizing anything that appears suspect. As a result, financial institutions must adopt a novel strategy that allows for rapid cross-channel data analysis and extraction while identifying fraud in real-time. In addition, AI provides fraud analysts with a complete overview of transactions, allowing them to examine past data in context.

4.  Facilitates digital businesses

To meet business goals, AI helps digital enterprises to reduce chargeback rates, decline rates, and operating expenses. One of the most valuable features of an AI-based fraud protection

technology is its capacity to personalize and adjust business results for the entire company, individual product lines, departments, and selling seasons in real-time. To achieve increasing agility, speed, and time-to-market levels, digital organizations depend on a combination of supervised and unsupervised machine learning, with AI-based fraud detection solutions at the heart of the endeavour.

5.  Accomplish Regulatory Compliance

Today, AI-based fraud protection helps companies comply with internal corporate regulations, regulatory authorities, and agreements with distribution partners. Financial institutions require a fraud detection system that uses AI and unsupervised machine learning to stay ahead of the game. Machine learning enables businesses to examine data in context across devices, applications, and transactions with minimal human intervention. This can save banks time and money by reducing the risk of expensive fines.

Given the large quantities of money at stake and the constant threat posed by criminals, firms will need to utilize cutting-edge technologies that can adapt to remain ahead. While businesses may not be able to transition to advanced data analytics right away, they should start by reviewing their existing data, determining data requirements, and creating the talent needed to get started as soon as feasible.