Security Analytics: What do you need to know

Jennifer Lepe • Dec 23, 2022

Security threats for IT assets are getting more frequent, and dangerously more sophisticated every day. In order to prevent these attacks, or have the best way to react to them, companies must have the proper data to act accordingly. That’s why Security Analytics is needed.

Security analytics is a combination of software, algorithms, and analytic processes used to detect potential threats to IT systems. The need for security analytics technologies is growing thanks to rapid advancements in malware and other methods of technological crimes.

Ideally, security analytics is a proactive approach to cybersecurity that uses data collection, aggregation and analysis capabilities to perform vital security functions that detect, analyze and mitigate cyberthreats.

How does it work?

The Data Science workflow has four well-defined phases:

  • Analyzing network traffic to detect patterns that indicate a potential attack.
  • Monitoring user behavior, especially potentially suspicious behavior.
  • Detecting insider threats.
  • Detecting data exfiltration.
  • Identifying accounts that may have been compromised.

Hackers use a wide range of attack mechanisms that exploit multiple vulnerabilities. Some threats can go undetected for months. Security analytics tools can keep track of common threat patterns and send alerts the moment an anomaly is discovered. This means transitioning from a protection approach to a detection one.



Security analytics structures data in such a way that it offers both a real-time and historical view of events. This provides a unified view of threats and security breaches from a central console and allows for smarter planning, faster resolution and better decision making. No matter how shallow or dismal it may look, any inconsistency shall remain suspicious.

One of the greatest benefits of implementing security analytics solutions is the vast volume and diversity of information that can be analyzed at one time. This way, organizations are able to easily connect the dots between various alerts and events. The result is proactive security incident detection and faster response times that help the business to protect the integrity of systems and data.

Security analytics are also helpful for companies to make sure of their compliance with government regulations and restrictions.

If you want to know more about using data to improve your company’s data security and integrity, let’s have a talk! We are more than happy to help you out with any questions you may have. Thanks for reading.

life after facebook analytics

By Jennifer Lepe • 23 Dec, 2022
June 30th was the last day Facebook Analytics was online. The free analytics tool was shut down by the social media giant quietly, allowing users to download all their reports and insights by the aforementioned date.  This change leaves marketers without a user-friendly, cost-effective analytics platform they could rely on. What can be done now? Here are some feasible and effective ways to get your analytics and insights reports from external tools to track your Facebook presence’s growth.

Open source quantum machine learning tools

By Jennifer Lepe • 23 Dec, 2022
When quantum physics and algorithms started to be integrated into machine learning tools, a whole new era of data science began. While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program. If you are looking forward to experimenting with this new technology, there are some open source tools available.

AI in a nutshell: How to get started in artificial intelligence

By Jennifer Lepe • 23 Dec, 2022
Artificial intelligence is one of the most significant breakthroughs of the 21st century. Experts from different industries study its capabilities and discover new ways of its application. The actual use of AI is pretty recent, however, scientists have been working around this concept since the 1950s.  The very concept of AI reminisces old movies and novels about robots and other sci-fi related themes, but the truth is that thanks to technologies such as machine learning and deep learning, AI became one of the most promising areas of the IT industry, and with this, one of the fastest growing.

Data Science workflow

By Jennifer Lepe • 21 Dec, 2022
So you have decided to walk the path of Data Science. That’s excellent news! Data Science is the way for you to take your analytics to a whole new level, bringing you tremendously accurate insights that will help your business grow.

The rise of machine learning in finance and fintech

By Jennifer Lepe • 20 Dec, 2022
We have reached a new era where automation has become the name of the game, and when it comes to automation, Machine Learning (ML) is a key technology to understand. Machine learning use has spread in several aspects of our lives today. It helps us get from point A to point B, suggests what to do with pressing issues, and is getting better at holding conversations. Is no surprise that in the world of finance we keep hearing about the combination of FinTech and machine learning. Applications of artificial intelligence (AI) in FinTech are predicted to be worth up to $7,305.6 million by 2022. Machine learning algorithms are a great tool for pattern identification. They are able to detect correlations among copious amounts of sequences and events, extracting valuable information that’s hidden among vast data sets. Such patterns are often missed or simply can’t physically be detected by humans and our limited senses. The ability of ML to learn and predict enables FinTech providers to recognize new business opportunities and work out strategies that actually make sense.  Let’s take a look at some of the practical uses of ML in Finance and FinTech.
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