Security & Privacy


How Machine Learning will become accessible to small businesses?

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Some technologies instantly become prize catches for businesses. Machine Learning (ML) and Artificial Intelligence (AI) are two of them. Big businesses already understood their potential in driving innovation and growth. According to Research and Markets, the worldwide ML market is going to expand with more than 44% compound annual growth in the years between 2017 and 2022. You shouldn't be surprised to know that small businesses account for a big portion of this growth story.


i-Invest Online The Value of Values: AI's Potential to Usher in a More Civilised Web

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A new international study commissioned by WP Engine and conducted by researchers at The University of London and Vanson Bourne explored the present and near future of artificial intelligence (AI)-driven human digital experiences on the web, and the often tenuous but also potentially rewarding relationship between consumers, brands and AI. The study, which surveyed consumers and enterprise companies (1,000 employees or more) in the US, UK and Australia, found that in an era of purpose-driven consumption, values--such as transparency, trust and humanness--are key drivers that unlock value in AI. According to IDC, worldwide spending on artificial intelligence (AI) systems is forecast to reach $35.8 billion in 2019, an increase of 44% over the amount spent in 2018. Much of that growth will come from the application of AI online because there is a natural, evolutionary symbiosis between AI and the internet. However, it was a sudden burst of activity starting in 2013 that marks the beginning of what we might term the modern AI period, especially for digital and digital experiences, characterised predominantly by automated content creation, programmatic ad buying in 2014, and intelligent search.


How can AI and Machine Learning Contribute to Enhancing Cybersecurity?

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Artificial intelligence (AI) is coupling with cybersecurity in order to create a new genre of tools known as threat analytics. Machine learning is allowing threat analytics to deliver greater precision in the areas of risk context, explicitly involving the behavior of privileged users, states a recent account in Forbes. This approach can be leveraged to develop notifications in real-time and respond actively to the incidents by cutting off sessions. FREMONT, CA: The general notion is that hackers have gone to the dark side to plan a massive attack on vulnerable businesses. Still, the truth is that the companies are not protecting their access credentials from easy hacks.


Machine learning in cybersecurity: One size doesn't fit all

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Did you know that there's a 96% shortfall in trained security analysts in the world? Organizations struggle to provide effective on-boarding and users find it difficult to understand the new security applications and processes. Micro Focus Fortify and ArcSight are leaders in the IT security domain. Our Adoption Readiness Tool (ART) delivers structured on-boarding, continuous enablement, and quick access to support content that boosts the user adoption of your security operations team. Reserve your seat for our webinar where you will learn about: • The 3 key ingredients to boost user adoption rates of your security software • Effective on-boarding with Fortify and ArcSight simulation-based training • Best practices for documenting security operating procedures and runbooks • Customer success stories You will also see live demos, showing how to auto-generate Try-Me simulations, product demonstration videos, and step-by-step runbooks.


Behind Scotiabank's Three-Pronged Approach To AI-Based Fraud Protection

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The fact that fraud is on the rise is not new, nor is it surprising that banks are turning to artificial intelligence (AI) and machine learning to fight back. Banks are, however, revamping their approaches to these technologies on how they may be applied outside of their typical use cases, fending off cybercriminals who have a growing number of opportunities to access online banking platforms and customer data. In the latest Digital Banking Tracker, PYMNTS looks at how banks are currently approaching their use of AI and machine learning in fraud protection and technology innovation. Competing in today's digital banking space is not as simple as opening a fully digital bank, as U.K. institution Barclays found. The bank has shuttered plans to open such a service in the U.S., stating that the project was proving too costly.


How AI can help fight cyberbullying

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Cyberbullying is just as dangerous and huge a problem as regular bullying. Last year, a 12-year-old girl from Florida committed suicide by hanging herself. As the investigation of her demise found out, the reason for her taking her life was cyberstalking and cyberbullying. Two more 12-year-olds were taken into custody after the incident for spreading rumors about the victim online and for inciting her to commit suicide. While bringing many new possibilities to education, technology and Internet accessibility can become a means to harm another person, as seen in this tragic case--and, unfortunately, many others.


AI vs. AI: Cybersecurity battle royale - Help Net Security

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No matter the generation, we all know some of the storied battles that have withstood the test of time. With AI projected to become a $190 billion industry by 2025 (according to Markets and Markets), it is more integrated in our everyday lives than we may even notice at this stage – and it continues to gain popularity. AI has found its way into home appliances, medical imagery, natural language processing and even musical composition. One area that AI has remained a constant is cybersecurity, where its continual learnings help detect and combat cyberthreats. But what if this technology were to fall into the wrong hands?


Deep in the dark: enhancing malware traffic detection with deep learning Tryolabs Blog

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Nowadays, network security is a business cornerstone of Internet Service Providers (ISPs), who must cope with an increasing number of network attacks, which put the integrity of the entire network at risk. Current network monitoring systems provide data with a high degree of dimensionality. This opens the door to the large-scale application of machine learning approaches to improve the detection and classification of network attacks. In recent years, the use of machine learning based systems in network security applications has gained in popularity. Such use usually consists of incorporating traditional (and shallow) machine learning models, for which a set of expertly handcrafted features is required to pre-process the data prior to training the models.


How machine learning is changing identity theft detection identity theft

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In the wake of several high-profile data breaches, companies, governments, and cybersecurity experts are calling for a more proactive approach to data protection. Using machine learning and artificial intelligence, cybersecurity experts are detecting identity theft faster and more efficiently than ever before. The Equifax hack in 2017 marked the beginning of a new era in data security. The sheer scope of the breach--with over 147.7 million Americans affected--embedded a sense of defeatism in data security. Many Americans have become apathetic to losing the privacy of their personal information, yet identity theft remains a $1.48 billion problem.


Making sense of the GDPR & Artificial Intelligence paradox

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The General Data Protection Regulation (GDPR) came into force in May 2018, to unify and regulate how data is processed, used, stored and exchanged for citizens and residents within the European Union (EU). While this law has been in effect for some time now, it still raises multiple questions for businesses around the world. This is especially true for both those who provide and those who leverage Artificial Intelligence (AI) while conducting business in the EU. AI is dependent upon a healthy flow of data in order to drive business growth and generate valuable business insights. Article 22 of the GDPR concerns automated profiling and decision making and outlines the ramifications for the incorrect use of data in these circumstances.