In today’s era of digital transformation, more and more organizations are evolving to become digital and leverage several business advantages, like increasing revenue, reducing operational costs, and accelerating time to market.
On the other hand, cyber-criminals are finding new ways and opportunities to target businesses. They are rapidly attacking digital aspects of an organization, like cloud infrastructure, software-as-a-service (SaaS), and connected devices.
Most of the businesses adopting digital technologies consider security as their biggest concern. According to an IDC report, enterprises will spend around $91 billion on security solutions in 2018. They are forced to adopt strong security services to minimize the risk of cyber exposure. This is why the demand for cyber security professionals is projected to grow three times as fast as other IT roles.
The traditional cybersecurity options no longer work in the digital world today. Hence, enterprises need to embrace modern technologies like artificial intelligence (AI) and machine learning.
These technologies can push strong security practices, reduce the attack surface, block malicious activities automatically, and protect businesses more efficiently.
Why Traditional Cybersecurity Practices no Longer Work?
There are several reasons traditional cybersecurity practices aren’t working today. As an increasing number of organizations are shifting their sensitive workloads to the cloud, all the data is kept at a central place which can be compromised despite the strong security solutions.
The website owners are using HTTP sites, without knowing its consequences. Further, as enterprises use modern technologies like data analytics solutions and IoT devices, a massive amount of data is generated every day. This data is backed up to the cloud to reduce costs, enhance accessibility, and minimize chances of data loss.
The attackers always try to stay a step ahead of the modern security solutions. Traditional security solutions have become outdated today, and attackers can compromise them easily.
For example, people use free website hosting to power their business websites, which is no doubt the riskiest of deals. The right thing is going through the reviews, checking the pros and cons before finalizing your hosting provider.
Embracing Artificial Intelligence for Cybersecurity
AI can definitely help in boosting cybersecurity practices. Here are some of the most important use cases.
#1. Gaining powerful insights about anomalies
Enterprise can leverage AI to find insights about malicious activities in the behavior patterns of data, applications, devices.
This can enable them to predict the upcoming threats to the business, and deploy SaaS Security solutions in advance to avoid a specific attack.
If organizations train the AI models properly, it can enable them to gain value from IT infrastructure, and the data generated and gathered from every context.
AI can analyze and tell enterprises what is normal and what is not. IT admins can use these insights to focus on high-value attacks and protect the organization.
#2. Enabling Secure Enterprise Mobility
Today, the employees in an organization not only work from the office, but also from home, cafes, airports, and other remote places. This is called enterprise mobility.
While enterprise mobility has opened windows to new opportunities, it affects the security perspectives of businesses.
Enterprises use tools that are generally developed for one environment or a system of record. IT admins push the best security practices for that one environment.
However, when users work remotely from other environments, the attack surface becomes extensively large. The users aren’t even aware of the security risks related to using open internet or other sources used for conditional access.
Hence, AI has become essential for enterprise mobility use cases. The AI-powered conditional access can analyze the behavior of users, data, devices, applications, location, and networks to provide secure data access to companies. This can significantly reduce the overall risk of exposure.
Moreover, AI can block access to malicious sites and data sets, and provide enterprises logging and reporting required for supporting automated actions.
#3. AI to Protect Against Malware
The traditional security systems are based on signatures to protect malware. However, these systems only block the threats and attackers that have been identified earlier. Today, new kind of attacks and malware are injected every hour which can bypass the traditional approaches.
When AI is infused with security systems, new types of attacks can also be detected. Machine learning and AI learn from the behavior and come to the rescue against both new and existing malware.
#4. AI for Authenticity
Another area of cybersecurity where AI can be used effectively is password protection and authenticity detection systems. Enterprises can use AI for protecting passwords and biometric logins.
AI can detect physical characteristics such as retina scans, fingerprints, etc. to boost the security of systems.
#5. AI for Fraud Detection
Fraud has been a big problem for many years now, especially for financial services organizations. With the rise in the number of financial transactions all over the world, the danger has also increased. With the enormous potential of reducing financial fraud, artificial intelligence comes to the rescue.
McAfee report found that it costs around $600 billion or 0.8% of the global gross domestic product because of cyber-crime in the economy. Credit card fraud is among the most widespread forms of cybercrime, which is continuously becoming worse in online transactions.
AI can be implemented in financial services organizations to identify the patterns in credit card behaviors that are different for every customer. Without AI, identifying these patterns will be really very complex as there are large volumes of customer data.
#6. AI for Botnet Detection
Botnets are groups of compromised computers used for creating a distributed denial of service (DDoS) attacks, phishing emails, and spreading viruses. Attackers are increasingly using botnets for several other purposes as well.
These can include spamming, sniffing traffic and keylogging, infecting new hosts, identity theft, attacking IRC chat networks, hosting illegal software on computers of victims, etc.
If enterprises properly implement AI in their IT infrastructure, it can detect the botnets and protect them from entering enterprise networks.
AI is still in the developing stage, but its use cases are vast in every field including cybersecurity. For best results in protecting against cyber-attacks, humans and AI should work hand in hand.
As AI and machine learning advance further, they will become a stronger fit for enterprises looking for robust security practices.