Modern day enterprise security is like guarding a fortress that is being attacked on all fronts, from digital infrastructure to applications to network endpoints. That complexity is why AI technologies such as deep learning and machine learning have emerged as game-changing defensive weapons in the enterprise's arsenal over the past three years. There is no other technology that can keep up. It has the ability to rapidly analyze billions of data points, and glean patterns to help a company act intelligently and instantaneously to neutralize many potential threats. Beginning about five years ago, investors started pumping hundreds of millions of dollars into a wave of new security startups that leverage AI, including CrowdStrike, Darktrace, Vectra AI, and Vade Secure, among others.

In the digitally driven world, there are endless numbers of solutions made available for addressing every type of threat. FREMONT, CA: The enterprises are welcoming more devices to the corporate network but are facing a cybersecurity challenge attack as constant threats are getting widespread. The panorama of data breaches, loss of essential data, or network crashes forces the enterprises to try out security measures. Nevertheless, they also force them to create cybersecurity strategies to guard the digital assets and level up with hackers and cybercriminals. Artificial intelligence (AI) has been a technology that is now primarily leveraged by enterprises as they realize that cyber threats have been a lot to manage without advanced technology.

Businesses today continue to be bombarded by an increasing number of cyberthreats, as hackers become adept at identifying and exploiting vulnerabilities in security systems. A survey by the World Economic Forum ranked data theft and large-scale cyberattacks 4th and 5th in a list of the biggest risks facing our world. With cybercrime regularly hitting the headlines, regulators are implementing new security guidelines and costly fines for violations. Adding to the pressure are consumers who are increasingly prepared to abandon business with a company if they've been hit by a data breach. Businesses can't afford to turn a blind eye to cybersecurity, which has now become a top priority for enterprises.

Machine learning, artificial intelligence and Autonomous services will require less human intervention in protecting enterprise infrastructure in the near future. Akshay Aggarwal, Director, Solution Specialist, Oracle India tells Brian Pereira how cyber security is evolving. And no, this is not straight out of a science fiction movie. All this is reality today. DC: The volume of security breaches is so high that it is not possible for humans to monitor and contain every single attack.

Cyber security company McAfee is announcing an expanded product portfolio that evolves security operations capabilities and allows for rapid response to today's threats. McAfee's updated Enterprise Security Manager (McAfee ESM 11) uses a new data architecture optimized for scalability, performance, faster search, and collaboration. This is combined with the newly launched McAfee Behavioral Analytics, and enhanced McAfee Investigator, McAfee Advanced Threat Defense, and McAfee Active Response. All of this is aimed at helping security operation teams to optimize their security infrastructure, leverage automation, improve detection, streamline workflows, and harness the power of human-machine teaming to improve response time and overall security outcomes. "With companies struggling to keep up with the current threat landscape, the need for human-machine teaming has never been greater," says Jason Rolleston, vice president of security analytics at McAfee. "Given the difficulty in finding skilled resources, enterprises need advanced analytics- and machine learning-powered solutions to augment the people they have.

Growing use of artificial intelligence, machine learning with data analytics, and business intelligence: Business applications continue to churn out large volumes of data, and users are trying to mine that data to determine patterns and predict user behaviour. In e-commerce, users want to know customers' buying patterns, which will help market products better. Website designers want to understand how visitors move through their sites in order to improve conversion rates. And companies want to analyse their sales data to correlate marketing dollars spent with sales dollars generated. Business intelligence and data analytics activities are becoming easier to perform, and that's driving their adoption in mainstream businesses that are seeking to make better, faster decisions. Rise of AI-powered chatbots in customer service and support: Over the past few years, chatbots -- the automated, human-like chat responders -- have been more of an experiment, with limited adoption.

What will be the key artificial intelligence and technology trends for IT in 2018? Business applications continue to churn out large volumes of data, and users are trying to mine that data to determine patterns and predict user behavior. In ecommerce, users want to know customers' buying patterns, which will help market products better. Website designers want to understand how visitors move through their sites in order to improve conversion rates. And companies want to analyze their sales data to correlate marketing dollars spent with sales dollars generated. Business intelligence and data analytics activities are becoming easier to perform, and that's driving their adoption in mainstream businesses that are seeking to make better, faster decisions.

Machine learning has moved enterprise security forward, allowing for visibility inside the network in order to better understand user behavior. However, malicious actors are using what is done with machine learning on the inside in order to attack the perimeter. Specifically, these types of attacks include DNS tunneling, attaching to Tor networks, and sending rogue authentication requests to directory services. Tom Gorup, security operations leader for Rook Security, said that in addition to these threats, "In general what we are seeing across the board is phishing, from wire fraud to distribution of malware. Generally we're seeing scans they're attempting to exploit."

Machine learning has moved enterprise security forward, allowing for visibility inside the network in order to better understand user behavior. However, malicious actors are using what is done with machine learning on the inside in order to attack the perimeter. Specifically, these types of attacks include DNS tunneling, attaching to Tor networks, and sending rogue authentication requests to directory services. Tom Gorup, security operations leader for Rook Security, said that in addition to these threats, "In general what we are seeing across the board is phishing, from wire fraud to distribution of malware. Generally we're seeing scans they're attempting to exploit."

The overall goal is to target treatment specifically to each individual so that clinical outcomes for that individual are optimized. One direction of attack is to use patient data to discover decision rules which specify the treatment to use as a function of a vector of features from the patient. Regression and classification are important statistical tools for estimating such rules based on either observational data or data from a randomized trial, and machine learning can help with this because of its ability to artfully handle high dimensional feature spaces with potentially complex interactions.