Hedge fund investments use several different strategies to achieve returns in both domestic and international markets. They are often aggressively managed and trade in land, real estate, stocks, derivatives, currencies, etc. markets.
The most lucrative hedge funds also have a diverse portfolio—which means investors need to analyze large amounts of data to make investment decisions.
As organizations continue to adopt IoT technology, ensuring secure access to a private network becomes particularly difficult. Network access control systems, however, protect network data by requiring user authentication and authorization before every request.
These systems also proactively address security breaches, though many also integrate with anti-virus or malware systems that organizations already use.
Network segmentation refers to the act of dividing different parts of a network into separate segments or subnets. This is done either physically or technologically, usually as part of a network access control system that limits who can access what parts of the network.
Once organizations have identified subnetworks, they establish virtual fences around them using a variety of techniques, including VLANs, SDNs, and firewalls.
Taking the place of an in-person managers, a financial robo-advisor provides personalized investment and wealth management services to individuals as well as small and medium-sized businesses.
Robo-advisors use deep learning and other artificial intelligence techniques to offer advice and even automate trades in a variety of industries and account types, from individuals with retirement accounts to small businesses with equity finance plans.
More than a static budget sheet in Excel, an AI financial planning program helps users set budgets and analyze their spending behavior. With a good ML program, company and personal budgets perform better—faster than via traditional methods.
A large proportion of data breaches come from authorized network users. Since they have network privileges, however, this type of cybersecurity threat is extremely difficult to address. Insider threat detection comprises the methods and technologies that organizations use to identify and mitigate these insider threats.
There are various types of insider threats, not all intentional or malicious. Pawns, for example, are simply victims of phishing or other social engineering traps while Goofs lost confidential data due to ignorant or arrogant flaunting of security policies. The malicious types of insider threats come from Collaborators and Lone Wolves, who are rarely encountered.