Deception Technology: in-Depth Guide [2019 update]

Though simple deception techniques such as honeypots have been around since 2000s, new deception technology techniques make deception technologies an emerging area to invest in for additional security measures.

What is deception technology?

Traditional network security consists of a limited number of responses which include logging, rejecting, quarantining intrusions. While these responses can block individual attacks, actions such as rejecting are visible to the adversary. Skilled adversaries can learn from these responses, increase the sophistication of their attack and test their target’s defenses. Instead, when dealing with intruders, companies can also rely on their attacker’s greatest weapon: deception. Read more


Top 20 Deception Technology Companies [2019 update]

Deception technology is a growing part of modern intrusion detection systems. See our article on deception technology to learn more. Read on to understand deception technology ecosystem and a comprehensive list of vendors.

Deception Technology Ecosystem

Incumbent security software providers

Companies like Symantec provide end-to-end cybersecurity solutions including solutions that include deception technology


Numerous startups have been launched since 2010s focusing on deception security.

Complete List of Deception Technology Vendors

* Date when tool was launched not the day the company was launched. This is because Symantec’s founding date is irrelevant when discussing their tool which provides features of deception technology.

** Only relevant for startups that focus on a single product because Symantec’s number of employees is irrelevant when discussing their specific deception security tool.

This is possibly an incomplete list in an emerging field, feel free to suggest other vendors in comments. And if you need to find the right AI vendors for information security:


AI Credit Scoring Models: In-depth Guide [2019 update]

Banks exist to make credit scoring decisions. Key function of banks is to enable individuals and companies to make expenditures before they can afford them. They can not accomplish this key function without becoming a competent estimator of who will pay back and who will default.

Banks always relied on models and experts to make effective credit decisions. Now models are becoming sophisticated enough to replace experts. AI companies are stepping in to provide these models to banks so they can focus on serving their customers.

What value do AI companies bring to credit scoring?

When it comes to credit scoring, banks already hold the most valuable data: repayments. Lona repayment track records are extremely valuable in understanding market dynamics and banks are the only institutions that hold granular data on repayments. They would need to share anonymized data on repayments with AI companies so they can build effective models.

While they lack unique data, AI companies bring model building expertise and AI talent. It is expensive to develop an in-house data science/AI team as a bank since

  • Tech enthusiasts find it less enticing to work in industries traditionally not considered tech
  • Data science talent is scarce and expensive
  • It takes time and effort to gather data other than loan repayment data. However granular data on topics such as the applicant’s income, career, education or expenditures can be quite valuable in credit scoring
  • Read more


    Auto Machine Learning Software/ Tools in 2019: In-depth Guide

    We explained autoML in detail. Now it is time to figure out the right software for auto ML for your business.

    3 Types of AutoML Solution Providers

    Open Source

    AI is one of the few scientific areas were despite significant corporate investment, even secretive tech giants like Apple publish their research findings. Therefore it should not be surprising that there are competitive open source autoML tools.

    All open source tools we came across, need an active development environment in Python or R and require the user to write at least a few lines of code to initiate the automated machine learning process.


    Given the scarcity of AI talent, AutoML can have a dramatic impact on democratizing AI and making companies more effective and efficient. Numerous startups are being launched and raise ambitious amounts to capture this opportunity.

    Most startups aim to offer tools that can be launched with a clean user interface by a non-technical user. Some tools also offer visualization to explain their findings and the resulting model.

    Tech giants

    Google claims to be an “AI-first” company and is delivering on that promise. Google Cloud AutoML is one of the first autoML tools launched by tech giants. IBM’s SPSS is one of the most popular analytics software providers and has been offering for various autoML tools such as auto classifier.

    Complete list of AutoML providers

    * Date when tool was launched not the day the company was launched. This is because Google’s founding date is irrelevant when discussing their Cloud AutoML tool.

    ** Number of employees on linkedin. Only relevant for startups that focus on a single product because Google’s number of employees is irrelevant when discussing their Cloud AutoML tool. This is also not relevant in open source where contributors are part time and therefore not directly comparable to a startup’s employees. We should add some github related popularity metrics here for open source projects.

    AutoML tools can greatly boost your company’s data science efforts. If you are not sure about which vendor to work with,


    Automated Machine Learning: In depth Guide [2019]

    Automated machine learning has the potential to greatly increase the productivity of data scientist and democratize machine learning tools. It can be a powerful solution to the well documented scarcity of data scientists.

    What is automated machine learning?

    According to Wikipedia:
    =&0=& (=&1=&) is the process of automating the end-to-end process of applying machine learning to real-world problems.
    Automated ML solutions aim to automate some or all steps of the machine learning process which includes:

    • Data pre-processing
    • Feature engineering
    • Feature extraction
    • Feature selection
    • Algorithm selection & hyperparameter optimization

    Since accuracy of machine learning solutions can be measured, automated systems can fine-tune data, features, algorithms and hyperparameters of algorithms to generate accurate models relying on established machine learning knowledge and trial&error.

    What are the benefits of autoML?

    Cost reductions

  • Increased productivity for data scientists
  • Democratization of machine learning reduces demand for data scientists
  • Read more