There’s no denying the crisis created by a sudden rise in automated phishing attacks. And it’s happening with more convincing content and greater accuracy due to Artificial Intelligence (AI), Machine learning, and Big Data. And while IT leaders are using AI to take security to the next level, what if this technology falls into the wrong hands- the bad guys?
The dawn of the internet and advances in computing means we’re able to trigger an exact solution to complex problems in diverse areas - ranging from astrophysics and biological systems to automation and precision. But at the same time, these systems are inherently vulnerable to cyber threats. In this fast-paced world where innovations come and go in the blink of the eye, cybersecurity remains top-of-mind, especially for companies dabbling with data-rich transformations such as the Internet of Things (IoT).
To a large extent, cybersecurity relies on file signatures to detect malware, and rules-based systems for detecting network abnormalities. Protection often stems from an actual virus outbreak – as security experts isolate the malicious files and identify unique signatures that help other systems become alert and immune. The same is true for the rules-based system: Rules are set based on experience of potential malicious activity, or systems are locked down to restrict any access to stay on the safe side. The only problem with these approaches is their reactive nature. Hackers always find innovative ways to bypass the known rules. Before a security expert discovers the breach, it’s often too late.
Traditional malware is designed to perform its damaging functions on every device they find their way into. One example is the NotPetya ransomware outbreaks, in which hundreds of thousands of computers were infected in a short period of time. This method works when the attacker’s goal is to inflict maximum damage. It’s not as effective if an attacker has a specific target in mind.
But the advent of disruptive technologies like Artificial Intelligence means our devices and applications are understanding us better. For example, an iPhone X uses AI to automatically recognize faces. While it’s a great feature, it creates an intricate puzzle where the chances of sensitive data going in wrong hands are high. Today, hackers are seen using the same technology to develop smart malware that can prey on targets by pinpointing them from millions of users.
With each passing year, attacks are getting more personalized with a higher likelihood of success. Hackers have even started using AI to accelerate polymorphic malware, causing code to constantly change and make it undetectable. Advanced tactics allow hackers to work around security to bypass facial security and spam filters, promote fake voice commands, and bypass anomaly detection engines.
The good news is this intelligence be used to protect the infrastructure as well. What makes AI cybersecurity unique is its adaptability. Intelligent cybersecurity doesn’t need to follow specific rules. Rather, it can watch patterns and learn. Even better, AI can be directly integrated into everyday protection tools – such as spam filters, network intrusion and fraud detection, multi-factor authentication, and incident response.
AI truly has become a game-changer for cybersecurity. Making cyber protection more effective, here are several specific areas where artificial intelligence can help:
Machine Learning - AI and machine learning (ML) are two different worlds; in fact, machine learning can be considered a subset of AI mainly used to enhance intelligence. When it comes to improving its cybersecurity, it automatically fills the skills gap to prevent cyber-attacks. If any malicious software is detected on the network, automatic incident response is sent. In addition, specific AI bots block access to websites completely. By preventing such actions, AI improves the security of an organization or individual on the internet.
Data Analysis - Data is the key to a successful cyber environment. Artificial Intelligence has the potential to identify the right data that gets the best result. With advanced data from these tools, we gain more comprehensive understanding of cyber threats at hand - determining the best practices to mitigate threats proactively.
Merging Technical and Human Approaches - The most powerful security approach combines the power of AI and human intervention. Machine learning is a great example. Artificial Intelligence helps break down complex automatic process for detecting attacks and reacting appropriately. But the ultimate challenge is delivering measurable results in methods that can then anticipate and detect attacks – and later analyze and prevent. As key functions are automated through such approaches as machine learning, this allows security experts to focus their attention on other areas to build a proactive and more reliable cyber defense plan based on data.
Cybersecurity and artificial intelligence will continue to evolve, but so will the persistence and innovation of hackers trying to access your data. There’s a new world of cyber protection – and Online Corpus can help.