As networks get more complex, more data is produced with increased speed, and companies dive deep on technology dependence, cybersecurity needs to gain efficiency. Analyzing and improving cyber security strategy is no longer a human-scale problem. In this scenario, artificial intelligence (AI) can be a big ally of cybersecurity, providing attacks prediction and rapid response to incidents, since it is able to quickly analyze millions of events and identify many different types of threats \u2013 from malware exploiting zero-day vulnerabilities to the identification of risky behaviors leading to phishing attacks or malicious code downloads. These technologies are continuously learning with each past event, leveraging this knowledge to identify new types of attacks today. The integration of AI in cybersecurity makes algorithms adapt through standard experiences rather than learning from cause and effect relationships. With AI, networks become more resistant against new cyber attacks, even as some types have not happened before, so that the company\u2019s data and systems can be more protected. AI can be used to detect \u201cbad guys\u201d, through a thorough analysis of network behavior, automating threat detection and responding more efficiently than traditional software-based approaches. Therefore, designing AI-based strategies for defense purposes has become an imperative. The objective is to add tools for the security team Cybersecurity still presents some unique challenges: A wide surface vulnerable to attacksHundreds of attack vectorsDeficit of qualified security professionalsMasses of data that defy human abilites But an AI-based self-learning cyber security management system should be able to solve many of these challenges. Since human beings are no longer able to adequately protect the dynamic corporate surface from attacks, AI provides the analysis and identification of threats that can be used by cybersecurity professionals to reduce the risk of breach and improve the security strategy. AI allows cybersecurity teams to form powerful human-machine partnerships. While artificial intelligence analyzes data at a speed that is impossible for humans to process, people are still better at making decisions in unusual situations, applying common sense and judgement where machines still can\u2019t. The capabilities of AI and Machine Learning are also evident in secure authentication: It can not only perform physical authentication through fingerprint or facial recognition, but also analyze how keys are written or typed in, the existence of typos, and record the typing speed of a possible attacker. AI and Machine Learning techniques such as User and Event Behavioral Analytics (UEBA) allow a computer to analyse the behavior of user accounts, of servers and endpoints, to identify anomalies that could trigger the beginning of an unknown attack. This process makes vulnerability management more effective by protecting organizations before security breaches are even reported and corrected. AI also offers scalability and constant updates, which are crucial in the current speedy data processing scenario. Security solutions need to evolve at the same speed in which attackers and hackers can create new scams, so it\u2019s no longer possible to use only traditional monthly-updated antivirus. With its own learning ability, artificial intelligence makes any protection solution more scalable and able to protect against old and new scams. Difficulties If artificial intelligence can be used to enhance cybersecurity solutions, it is also a resource for strengthening attacks. While hackers increasingly use AI-based threats, the use of the same technology as a defense is paramount. With the application of artificial intelligence, it\u2019s possible to arm malicious codes, hide malign codes in benign applications, create attacks capable of self-propagating and find vulnerabilities on security systems. The artificial neural networks themselves can help hackers discover the weaknesses of information safeguarding systems. Technology still manages to simulate user's behavior to hide in the plethora of actions performed by real users of a company when carrying out activities that seem regular. This tactic helps the attack to escape detection, that is, to camouflage itself from traditional security tools. So, if your cybersecurity depends solely on human monitoring capabilities, you are in trouble. Against AI, there should only be another AI, capable of responding in milliseconds to cyber attacks that would take minutes, hours, days or even months if they were to be identified by humans. Using AI for good Many companies are already using AI in their cybersecurity initiatives or plan to introduce it soon. A Capgemini Research Institute study with 850 executives, revealed that almost three-quarters of them (73%) already had AI use cases for cybersecurity being tested in their companies in 2019. But the effective integration of artificial intelligence technology into your existing cyber security systems is not something that can be done overnight. It takes planning, training and basic preparation to ensure your systems and employees can use it to its fullest advantage. In other words, you need to build a roadmap for AI in cybersecurity implementation. A journey that begins with identifying data sources and creating data platforms to operationalize AI, as well as selecting the right use cases to accelerate and maximize benefits. And that also involves collaborating externally to improve threat intelligence, deploy Security, Orchestration, Automation and Response (SOAR) processes to improve security management, train professionals to be ready to use AI, and it ends with establishing strong governance, focusing on long-term improvements. Obtaining the resources to use AI is one of the difficulties that companies face when looking for the right security solution. Building and maintaining artificial intelligence systems depends on a large investment of time and money. Cybersecurity solutions built on AI structures are not cheap. As such, they have been prohibitively expensive for many companies - small to medium-sized businesses (SMBs) in particular. According to projections by Meticolous Research, investments in artificial intelligence in the cybersecurity market are expected to create a 23.6% CAGR from 2020 to 2027, reaching US$ 46.3 billion in 2027. Nonetheless, let's be realistic: as expensive as it is, it is much cheaper to pay for effective cybersecurity solutions than to pay fines, downtime and other costs associated with successful cyber attacks. The main message we want to convey is that it is critical to ensure that you have the right systems, training and resources to effectively manage and use AI cybersecurity solutions. This will help you to reduce the risks associated with using artificial intelligence security tools.