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Cyber Security

Threat Intelligence Necessities

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Threat intelligence is a necessary element of any cybersecurity program because without it, vulnerability management is almost impossible. As of this writing, NIST had published 161,463 vulnerabilities, any one of which may or may not be relevant to a particular company.

Without threat intelligence, security teams tend to prioritize vulnerabilities based on their severity. Bad actors know this, which is why they often exploit medium severity vulnerabilities. Conversely, with threat intelligence, organizations can focus on and prioritize the vulnerabilities that are relevant to them.

Threat intelligence is not one thing, however. Like other aspects of cybersecurity, it involves different kinds of technology, some of which overlap in terms of capabilities.

“Intelligent” threat intelligence

AI is seeping into every imaginable type of software, including cybersecurity tools. In a threat intelligence context, AI identifies potential threats, analyzes them and helps to resolve the issue (automatically, by making recommendations and/or escalating the matter to cybersecurity professionals).

Cyber automation is necessary because so many attacks are automated. If enterprises fail to automate threat intelligence, they’re fighting a battle they can’t possibly win. 

Some of the things cyber automation can do include:

  • Assessing threats in the network;
  • Preventing an attack from advancing further;
  • Proactively preventing a threat;
  • Identifying groups of threats which behave similarly; and
  • Enabling predictive vulnerability assessment.

The intelligence aspect of cyber AI detects and analyzes threats. It also speeds remediation by taking proactive action or providing security professionals with actionable intelligence. 

SIEM/SOAR

Security Information and Event Management (SIEM) analyzes the security alerts generated by applications and network hardware and generates security logs that can be used for reporting, compliance and auditing purposes. SIEM is available in different forms including software, hardware (appliances) and managed services. 

In addition to aggregating data from various sources, SIEM also correlates data and provides an automated analysis of correlated events which are displayed in a dashboard. The data it collects can be used for forensic analysis, to detect configuration issues, to identify attackers and victims and to pinpoint various types of activity including brute force, denial of service and zero-day attacks.

Security Orchestration, Automation and Response (SOAR) also provides essential intelligence. Its security orchestration capabilities integrate various tools such as behavioral analytics, endpoint security, firewalls, intrusion detection and prevention systems (IDSs and IPSs), SIEM and more. Its automation capabilities enable continuous monitoring, the automation of previously manual tasks such as vulnerability scanning, automated response and handoffs to security professionals as needed.

IDS, IDR, IPS

Intrusion detection systems (IDSs) followed the introduction of firewalls when it became obvious that firewalls could not keep hackers out of systems and networks. IDSs detect a breach in an effort to minimize the potential damage. Today’s IDSs are either signature-based or anomaly-based.

One of the challenges with traditional IDSs is their lack of response intelligence. While traditional IDSs can identify what happened, they don’t include the recommendation aspect which is present in more modern Intrusion Detection and Response (IDR) options. With the help of AI, the IDR systems are constantly identifying and monitoring threats and accelerating remediation.

Intrusion prevention systems (IPSs) proactively identify threats through continuous monitoring. They detect patterns that suggest potential incidents and threats. Unlike IDSs, their purpose is to prevent attacks from happening in the first place.

OSINT and Dark Web

Open-source intelligence (OSINT) uses publicly available resources such as information that can be found on the Internet, in the media, in research papers or journals, on corporate websites, in IP databases (e.g., the U.S. Patent and Trademark office (USPTO), social media and more. This information can be combined with information on the Dark Web to help identify cybercriminals. OSINT can be used to identify data hackers could use against an organization such as information about employees, IT assets or the company.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.cshub.com/executive-decisions/articles/threat-intelligence-necessities

Big Data

Konsentus Verify supports checking of UK-RTS compliant certificates

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Konsentus Verify supports checking of UK-RTS compliant certificates

Konsentus today confirmed that its open banking third party provider (TPP) identity and regulatory checking solution, Konsentus Verify, can validate the identity of TPPs regardless of whether a UK-RTS compliant digital certificate or EEA issued eIDAS certificate is presented. 

This follows OBIE’s recent announcement that UK-regulated TPPs must complete their migration from OBIE Legacy Certificates to UK-RTS compliant certificates (OBWACs/ OBSEALs) no later than 30 June 2021 by which time they must also have revoked any active OBIE Legacy Certificates. 

From the end of June 2021, ASPSPs must reject the use of OBIE Legacy Certificates for PSD2 identification purposes ensuring they only accept certificates that are compliant with the UK-RTS. 

Konsentus Verify provides TPP identity and regulatory checking services to protect Financial Institutions from the risk of open banking fraud.  The identity checking element of the Konsentus solution is based on the validation of a TPP’s digital identity certificate.  

Konsentus Verify checks in real-time a certificate’s validity and whether it has been issued by a trusted certificate issuer. In addition, Konsentus Verify checks the Payment Services a TPP is authorised to provide by its home country National Competent Authority.

However, digital identity certificates are not usually updated over a certificate’s lifespan and do not list the roles a TPP can perform outside the TPP’s home country. Any ‘Passporting’ information must be obtained for each country the TPP wants to provide services into.

Any EEA TPP wanting to access accounts held by a UK-based ASPSP must either be on the FCA’s Temporary Permissions Regime list or registered directly with the FCA. Konsentus Verify validates in real-time the legitimacy and current authorisation status of TPPs providing payment services in the UK regardless of whether an eIDAS or UK-RTS compliant certificate is presented.

Mike Woods, CEO Konsentus commented, “With over 200 UK TPPs regulated to provide open banking services in the UK, we can offer our customers a single solution that means both UK-RTS compliant certificates and eIDAS certificates can be checked without having to introduce additional processes or delays. No matter where the transaction is taking place or where the TPP is located, we offer our customers a single solution providing identity and regulatory checking at the time of the transaction.”

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.fintechnews.org/konsentus-verify-supports-checking-of-uk-rts-compliant-certificates/

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Big Data

How big data impacts the finance and banking industries

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Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. These terms are fundamentally tied predominantly to matters involving digital transformation as well as growth in companies. In this modern age, each business entity is driven by data. Data analytics are now very crucial whenever there is a decision-making process involved.

Through this tool, gaining better insight has become much easier now. It doesn’t matter whether the decision being considered has huge or minimal impact; businesses have to ensure they can access the right data to move forward. Typically, this approach is essential, especially for the banking and finance sector in today’s world.

The Role of Big Data

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. They have to utilize Big Data to its full potential to stay in line with their specific security protocols and requirements. Banking institutions actively use the data within their reach in a bid to keep their customers happy. By doing so, these institutions can limit fraud cases and prevent any complications in the future.

Some prominent banking institutions have gone the extra mile and introduced software to analyze every document while recording any crucial information that these documents may carry. Right now, Big Data tools are continuously being incorporated in the finance and banking sector.

Through this development, numerous significant strides are being made, especially in the realm of banking. Big Data is taking a crucial role, especially in streamlining financial services everywhere in the world today. The value that Big Data brings with it is unrivaled, and, in this article, we will see how this brings forth positive results in the banking and finance world.

The Underlying Concept

A 2013 survey conducted by the IBM’s Institute of Business Value and the University of Oxford showed that 71% of the financial service firms had already adopted analytics and big data. Financial and banking industries worldwide are now exploring new and intriguing techniques through which they can smoothly incorporate big data analytics in their systems for optimal results.

Big data has numerous perks relating to the financial and banking industries. With the ever-changing nature of digital tech, information has become crucial, and these sectors are working diligently to take up and adjust to this transformation. There is significant competition in the industry, and emerging tactics and strategies must be accepted to survive the market competition. Using big data, firms can boost the quality and standards of their services.

Perks Associated with Big Data

Analytics and big data play a critical role when it comes to the financial industry. Firms are currently developing efficient strategies that can woo and retain clients. Financial and banking corporations are learning how to balance Big Data with their services to boost profits and sales. Banks have improved their current data trends and automated routine tasks. Here are a few of the advantages of Big Data in the banking and financial industry:

Improvement in risk management operations

Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. It improves the response timeline in the system and consequently boosts efficiency. Big Data provides financial and banking organizations with better risk coverage. Thanks to automation, the process has become more efficient.Through Big Data, groups concerned with risk management offer accurate intelligence insights linked to risk management.

Engaging the Workforce

Among the most significant perks of Big Data in banking firms is worker engagement. The working experience in the organization is considerably better. Nonetheless, companies and banks that handle financial services need to realize that Big Data must be appropriately implemented. It can come in handy when tracking, analyzing, and sharing metrics connected with employee performance. Big Data aids financial and banking service firms in identifying the top performers in the corporation.

Client Data Accessibility

Companies can find out more regarding their clients through Big Data. Excellent customer service implies outstanding employee performance. Aside from designing numerous tech solutions, data professionals will assist the firm set performance indicators in a project. It will aid in injective analytic expertise in multiple organizational areas. Whenever there is a better process, the work processes are streamlined. The banking and financial firms can leverage improved insights and knowledge of customer service and operational needs.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.fintechnews.org/how-big-data-impacts-the-finance-and-banking-industries/

Continue Reading

Big Data

How big data impacts the finance and banking industries

Published

on

Nowadays, terms like ‘Data Analytics,’ ‘Data Visualization,’ and ‘Big Data’ have become quite popular. These terms are fundamentally tied predominantly to matters involving digital transformation as well as growth in companies. In this modern age, each business entity is driven by data. Data analytics are now very crucial whenever there is a decision-making process involved.

Through this tool, gaining better insight has become much easier now. It doesn’t matter whether the decision being considered has huge or minimal impact; businesses have to ensure they can access the right data to move forward. Typically, this approach is essential, especially for the banking and finance sector in today’s world.

The Role of Big Data

Financial institutions such as banks have to adhere to such a practice, especially when laying the foundation for back-test trading strategies. They have to utilize Big Data to its full potential to stay in line with their specific security protocols and requirements. Banking institutions actively use the data within their reach in a bid to keep their customers happy. By doing so, these institutions can limit fraud cases and prevent any complications in the future.

Some prominent banking institutions have gone the extra mile and introduced software to analyze every document while recording any crucial information that these documents may carry. Right now, Big Data tools are continuously being incorporated in the finance and banking sector.

Through this development, numerous significant strides are being made, especially in the realm of banking. Big Data is taking a crucial role, especially in streamlining financial services everywhere in the world today. The value that Big Data brings with it is unrivaled, and, in this article, we will see how this brings forth positive results in the banking and finance world.

The Underlying Concept

A 2013 survey conducted by the IBM’s Institute of Business Value and the University of Oxford showed that 71% of the financial service firms had already adopted analytics and big data. Financial and banking industries worldwide are now exploring new and intriguing techniques through which they can smoothly incorporate big data analytics in their systems for optimal results.

Big data has numerous perks relating to the financial and banking industries. With the ever-changing nature of digital tech, information has become crucial, and these sectors are working diligently to take up and adjust to this transformation. There is significant competition in the industry, and emerging tactics and strategies must be accepted to survive the market competition. Using big data, firms can boost the quality and standards of their services.

Perks Associated with Big Data

Analytics and big data play a critical role when it comes to the financial industry. Firms are currently developing efficient strategies that can woo and retain clients. Financial and banking corporations are learning how to balance Big Data with their services to boost profits and sales. Banks have improved their current data trends and automated routine tasks. Here are a few of the advantages of Big Data in the banking and financial industry:

Improvement in risk management operations

Big Data can efficiently enhance the ways firms utilize predictive models in the risk management discipline. It improves the response timeline in the system and consequently boosts efficiency. Big Data provides financial and banking organizations with better risk coverage. Thanks to automation, the process has become more efficient.Through Big Data, groups concerned with risk management offer accurate intelligence insights linked to risk management.

Engaging the Workforce

Among the most significant perks of Big Data in banking firms is worker engagement. The working experience in the organization is considerably better. Nonetheless, companies and banks that handle financial services need to realize that Big Data must be appropriately implemented. It can come in handy when tracking, analyzing, and sharing metrics connected with employee performance. Big Data aids financial and banking service firms in identifying the top performers in the corporation.

Client Data Accessibility

Companies can find out more regarding their clients through Big Data. Excellent customer service implies outstanding employee performance. Aside from designing numerous tech solutions, data professionals will assist the firm set performance indicators in a project. It will aid in injective analytic expertise in multiple organizational areas. Whenever there is a better process, the work processes are streamlined. The banking and financial firms can leverage improved insights and knowledge of customer service and operational needs.

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.fintechnews.org/how-big-data-impacts-the-finance-and-banking-industries/

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Cyber Security

The Hidden Challenges of Data Retention

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Companies are drowning in enterprise data. While such data can serve as a conduit to innovation, it can also be a liability.

Having the right data retention policies in place not only protects data from unauthorized access or other malfeasances, it also ensures data is primed for business usage. Furthermore, recent regulations such as GDPR mandate the creation of a data retention policy to prove data is properly managed and utilized throughout its entire lifecycle, but especially at the very end.

Data Deletion 

While many organizations excel at saving data, few have mastered data disposal. 

According to a 2020 Deloitte survey, while 80% of companies surveyed have a defined data retention policy in place:

“only one out of three respondents provided data to the business process owners for final disposition. Data is seldom reclassified or anonymised per current practices. Organisations may not be aware of techniques to use anonymised/pseudonymised data in an effective manner. Only 30 percent of the organisations were adopting automated erasure techniques for data on completion of the retention period.”

Furthermore, the report found that an alarming number of companies relied on ineffective data deletion and drive/device formatting methods that can leave sensitive data unprotected. In fact, more than 15% of second-hand drives purchased from an online retailer contained leftover data from the previous users. 

GDPR and like-minded regulations also require proof of data disposal in the event of a consumer complaint. However, this too has been woefully overlooked as only 32% of companies “are prepared for and may have conducted audits of processing activities with respect to end-of-life of personal data.” 

It is clear that CISOs need to become involved with the data retention process. Though policy decisions can be left to chief data and privacy officers, CISOs are increasingly being compelled to oversee the execution of data retention strategy, especially when it comes to the logging and verification of data disposal. 

Data Lake Security & Governance 

Over the past decade, data lakes have surged in popularity amongst data scientists looking to experiment with advanced analytics. However, if not properly maintained, data swaps can easily devolve into data swamps whereby the system is flooded with irrelevant, unusable data. 

Such an environment poses a number of data security and privacy risks. To start with, data that can’t be found can’t be disposed of or retrieved in response to subject access requests. 

Secondly, even well governed data lakes are vulnerable to false data injection and malware obfuscation as datasets are not segmented by clear boundaries. As a result, someone with access to a particular file object can modify it, and there is no trail or history of what was modified.

CISOs, CDOs and CPOs must work together to create security-first data governance frameworks for data lakes to protect the business, it’s customers and it’s most valuable strategic data assets. Such a plan should also address:

  • Data access control 
  • Data protection (encryption)
  • Data lake usage audit 
  • Data leak prevention 
  • Data lineage documentation

In the event the business opts to “drain the data swamp” it’s critical for the CISO to play an active role in determining what data to keep and how to dispose of unusable or corrupted data in the securest way possible. 

Coinsmart. Beste Bitcoin-Börse in Europa
Source: https://www.cshub.com/executive-decisions/articles/the-hidden-challenges-of-data-retention

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