There's a never-ending battle going on between cyber defenders and attackers, and this plays out with security products too: As soon as a security vendor develops a way to mitigate the latest threat, attackers are busy finding a way around it or a new threat to take its place. To try to gain an edge in their efforts to protect businesses and individuals from scammers, malware, and data theft, many cybersecurity companies have turned to artificial intelligence (AI) and machine learning (ML) as a potentially useful weapon in their arsenal. There are some benefits to employing AI in a cybersecurity context. It can make defensive measures stronger and response times faster, but it's not a perfect solution. AI is not a replacement for human intelligence--especially when it comes to identifying and mitigating threats--but in the right contexts, with the right team, it can be helpful.

Modern cyber attackers' tactics, techniques, and procedures (TTPs) have become both rapid and abundant while advanced threats such as ransomware, cryptojacking, phishing, and software supply chain attacks are on an explosive rise. The increasing dependence global workforces have on digital resources adds another facet to a growing cyber attack surface we all now share. In an effort to stand up to these challenges, businesses task their CISOs with developing, maintaining, and constantly updating their cybersecurity strategies and solutions. From a tactical standpoint, CISOs ensure that their business's security architecture can withstand the ever-shifting modern threat landscape. This means choosing the right tool stack that is capable of combating complex cyber threats at the breakneck speed in which they appear.

Cyberattacks have never been more successful than in the last year, and for 2020 an alarming increase of cybercrimes is expected, factoring in the surge of critical IT assaults that arose with the spread of COVID-19. And in a world where the enormous capabilities of artificial intelligence (AI) and machine learning (ML) are continuously growing, the potential for attackers to exploit them cannot be ignored. In 2019, four out of five organizations were victim to at least one successful attack to their IT security, according to CyberEdge's annual Cyberthreat Defense Report (CDR). And with the number of corporate endpoints becoming more and more with the increased need for remote working, the traditional cybersecurity strategy based on the detection of malware starting from what is external to the company perimeter is not feasible anymore. That's why IT professionals are most concerned about the security of IT components that are relatively new such as containers, or those that are infrequently connected to the corporate network and harder to monitor, such as tablets, mobile phones and other IoT devices.

However, an AI system is only as good as the data being fed into it. Just like a child, if you teach a child bad behaviors, those behaviors will be carried on with them as an adult. If the information being fed to an AI system is intentionally malicious or inaccurate, the AI system will learn to behave the way the attacker wanted it to, not the way the system was designed to behave, said Steve Tcherchian, chief information security officer at XYPRO and a regular contributor to and presenter at the EC-Council, a cyber security technical certification body. In less important cases, this can be a mild annoyance. He cites the example of smart homes.

If, as we have seen in recent months, the rate of digital transformation has gone beyond anything we have seen in the past, it has also opened up many enterprises to attack in ways that have never been possible until now. Each time an organization adds new technology to the digital workplace it exposes itself to new risks. However, there are also new ways to protect their digital assets, just as there are new ways to ensure productivity. At the end of last year, Capgemini released research into how organizations are turning to artificial intelligence (AI) to protect their digital properties. Titled Reinventing Cybersecurity with Artificial Intelligence, it showed that 42% of the companies studied had seen a rise in security incidents through time-sensitive applications.

As enterprises face a barrage of cyberattacks, and the nature of these attacks is growing in sophistication, it is becoming difficult to pinpoint the vulnerabilities. The bad actors are becoming smarter and more coordinated. It has become a very organized industry, even though it is a dark industry. So here are three key aspects that organizations must immediately address as they prepare their cybersecurity strategy for 2020. Firstly, enterprises need to be in a state of perennial alert.

Bottom Line: Attacking endpoints with AI, bots, and machine learning is gaining momentum with cybercriminals today with no signs of slowing down into 2020, making endpoint security a must-have cybersecurity goal for next year. Cyberattacks are growing more complex and difficult to prevent now and will accelerate in the future, making endpoint security a top goal in 2020. Cybercriminals are using structured and unstructured machine learning algorithms to hack organizations' endpoints with increasing frequency. Endpoint attacks and their levels of complexity will accelerate as cybercriminals gain greater mastery of these techniques. In response, endpoint protection providers are adopting machine learning-based detection and response technologies, providing more cloud-native solutions that can scale across a broader range of endpoints, and designing in greater persistence and resilience for each endpoint.

One need not understand the finer details of the Dark Web in order to respect the risk these black markets pose in an increasingly data-centric world. In nearly every aspect of our modern lives, cybersecurity is becoming a necessary part of the conversation. Even novice "hackers" can rent cloud-based botnets and orchestrate DDoS attacks for around $25 per hour according to data released by Kaspersky Labs. Fortunately, the classic adage remains true, even on the Dark Web - you get what you pay for. Low-priced DDoS attacks are easily thwarted by modern network security systems designed to recognize incoming threats and divert resources to squash attacks. Thanks to advancements in Artificial Intelligence (AI) and Machine Learning (ML) in the area of cybersecurity, small businesses and enterprise-level companies can stay focused and maintain a high level of digital trust from their customers while keeping overhead costs in check.

Every time you connect to the internet from a computer, tablet or smartphone, there is a growing risk of cyberattack. If the threat is aimed at your workplace, then the entire organization around you could be vulnerable as well and, too often, the result is a major data breach. A well-run company, regardless of its size or global reach, must eventually acknowledge that cybersecurity requires a significant investment. But what tools and processes return the most bang for your buck? A growing number of experts believe that new technology based on machine learning and artificial intelligence are where the smart money lies when it comes to computer, network and data security.

An ever-present threat to any given country's national security is that of cybersecurity. There are always hackers that want to use technology for malicious purposes, not to say the long list of adversaries that a country can pile up along the years. That's so as what it is at stake is millions of sensible data from citizens, companies, directories, senior officers and members of the government, state's papers and more. Unfortunately, not all Governments take this peril as seriously as they should, and the efforts towards creating cyber-defense strategies – in most countries – lack budget, personnel and even real, field knowledge. Before this absence of real policies, Artificial Intelligence might be well seen as a good starting point where to build the walls that keep out any possible threats.