Security News

AI is not going to solve your cybersecurity problems, so can we stop pinning our hopes on it? Instead of seeking a "Magic box" to solve all our problems, organizations should be looking at how skilled personnel can work with AI to utilize the strengths of each to improve the other. Think how many possible company setups there are, applications you can run, people you can work with, economic factors that can impact a company, and so on.

Insurers' use of predictive analytics to fight fraud has reached an all-time high, according to an insurance fraud technology study by the Coalition Against Insurance Fraud and SAS. The study reveals that 80% of insurers use predictive modeling to detect fraud, up from 55% in 2018. "The shifts we've seen since the 2018 study emphasize the increasingly sophisticated technologies needed to foil insurance fraudsters' criminal exploits," said David Hartley, Director of Insurance Solutions at SAS. "Predictive modeling is up 25%. Text mining has nearly doubled, jumping from 33% to 65% in three years. These findings prove that, even as COVID-19 has fueled rampant fraud, insurers are agilely stretching their advanced analytics capabilities to counter rapidly changing threats."

Urban surveillance and public safety technologies are finding new use cases following the COVID-19 pandemic and increasing AI capabilities. ABI Research forecasts a CAGR of 11.6 % with 1.4 billion Closed-Circuit Television surveillance cameras in urban areas worldwide in 2030.

In this interview with Help Net Security, Scott Laliberte, Managing Director at Protiviti, talks about the implementation of AI and ML in cybersecurity programs, why this is a good practice and how it can advance cybersecurity overall. To adopt these new technologies, the organization must not only change its existing approaches, but also change the mindset of its people and its culture in order to really embrace them.

Many organizations are looking for AI to make sense of tremendous amounts of unstructured data that has been collected about people, transactions, systems, and social connections. Video surveillance systems are a major concern, with AI now being able to identify and track people from networks of connected camera systems.

To delay it, highly advanced AI-powered security that protects your entire suite is needed. AI is integral for any good email security solution.

In the Foreword to this year's survey, NewVantage Partners CEO Randy Bean, and Thomas H. Davenport, a Fellow with the firm, write "The ten years of the survey provide a useful measure of progress-or the lack thereof in some respects-in how companies are managing these important initiatives. From 2012 to 2022 the survey has assessed the initiatives that large companies are focused on, where they are investing and the returns they are getting, the roles assigned to manage data, and the issues that cause significant challenges." The state of data and AI initiatives Investment in data and AI initiatives continues to grow as efforts deliver measurable results.

Those are a proper understanding of what AI is capable of and how it should be used, and improvements to the security of AI. To understand how machine learning works and how to use it properly, it is important to bear in mind that although some ML models are very complex, the systems incorporating ML are still just a product of combining an understanding of a domain and its data. Model evasion attacks essentially exploit the fact that decision boundaries in the model are very complex and the capability of the model to interpolate between samples is limited, in a way leaving "Gaps" to be utilized for.

Ramamoorthy is firmly on the affirmative side for using AI to fight cybercrime. "Attackers use powerful techniques like AI to exploit unsuspecting end-users to gain access to privileged information by compromising said access points."

In a report released on Wednesday, consulting firm Deloitte describes two tools that can make AI tasks such as machine learning more private and secure. There are some technological obstacles to using HE and FL. Processing encrypted data with HE is slower than processing unencrypted data.