Autonomous AI Cybersecurity
In a recent post in Edge Computing News, our CEO, Inna Ushakova, covers the plethora of modern challenges traditional cybersecurity measures can no longer cope with. She then addresses how AI-driven automation is capable of identifying risk points in users, devices, or locations at incredible speed, scalability, and accuracy, beyond what humans can handle.
Thanks to increased digitization, greater data volumes, and unstable conditions, the surface for cyber attacks grows larger. To discover how AI and automation can boost security operations’ visibility and efficiency, please read the full post here.
Overwhelming volume and speed of cyber attacks
The frequency and number of security incidents demand a new operational strategy. This dramatic increase creates an even larger attack area. In fact, by the end of 2021, a reported 74 zettabytes of data were created according to expert estimations. That means 74 zettabytes of data that can be exploited.
Why is the situation today so dire? In short, the pandemic sped up the shift to digital, which increased both opportunities and risks. Now, there are a lot more people who work from home. More people are using the cloud meaning there are more cloud services and integrations of key systems with third-party partners. A huge number of IoT devices are at the edge sending data to the cloud. All of them are connected to each other and depend on each other. This provides sophisticated connectivity and creates value at speeds and sizes that weren’t possible even a few years ago, resulting in huge increase of cyberattacks.
Looking into the future, things show no sign of slowing down. Studies show that by 2025, the amount of data generated each day will be 463 exabytes globally and by 2030, 90% of people older than 6 will be digitally active.
It’s no wonder that today’s executives find cyber risks as the most pressing challenge. In fact, across industries, the market fears an increase in cybersecurity attacks, which shows why the projected spending on cybersecurity service providers by 2025 is set to increase to $101.5 billion. This is coupled with the fact that the annual increase of costs related to cybercrime is 15% and it will reach $10.5 trillion in 2025.
Even though advanced technology services have made things more efficient, many organisations are slowly realising that their digital footprints are full of unknowns and complicated things. On top of that, security teams are typically understaffed, lack expertise, and overwhelmed by too much data from different sources, too many tools, and not enough insights. Even the most knowledgeable security experts and the largest, most skilled cybersecurity operations teams may not be able to handle these problems.
Unsurprisingly, over 90% of cyberattacks are made possible, to a greater or lesser extent, by human error, according to IBM data. The reality is that cyber risk management hasn’t kept up with the growth of digital and analytics transformation, and many companies don’t know how to find and deal with digital risks. Now, cybersecurity requires a new, more forward looking approach that includes threat intelligence software and AI-powered automation.
What’s causing cyber risks to soar?
Let’s take edge computing as the first angle. Edge computing has a lot of different benefits for many industries, giving them clear advantages over their competitors. But it also brings security vulnerabilities. We are witnessing as more companies of all sizes and from all sectors are becoming the victims of bad actors, losing money and reputation. The situation looks like hackers are more strategic—they invest in advanced AI tools to exploit all possible channels and often win the cybersecurity race.
The footprint of edge and IoT devices is only growing by the day, and it’s naive to think that humans are capable of monitoring and figuring out all the existing cybersecurity dangers. And if left unchecked, these dangers can lead to destroyed networks, shutdown of operations, or even endangering human lives.
We now enter into the realm of what’s known as weaponised AI, which is when AI systems are changed or new AI programs and tools are made to hurt performance and mess up normal operations. The goal of cyber attacks that use AI as a weapon is to get into networks and systems faster than most organisations can defend themselves. AI is used to do this by making use of its unique skills.
From this angle, cybercriminals also use AI to increase the size of their attacks and make them more effective. AI can learn to recognise patterns in behavior, so it can figure out how to trick people into thinking that a video, phone call, or email is real, then get them to break into networks and give up sensitive information. All of the social tricks cybercriminals use now can only be made much worse with the help of AI.
From all angles, threats are real and dangerous, which is why IT security professionals should turn to AI and ML to enforce good security practices and reduce the attack surface. Simply put, AI and automation can solve complex problems quickly and consistently in a way that human intelligence can’t.
Automation in cybersecurity with AI
As former Cisco CEO John Chambers once said:
“I think there are two types of companies; those that have been hacked and those that have not yet realised it.”
AI and automation boost security operations’ visibility and efficiency, which is why 93% of organisations are increasingly interested in adopting or are considering adopting this approach. We envision four ways in which AI technologies can change security:
- Machine learning helps find patterns, keep track of new assets and services, and improve the way AI models work.
- Deep Reinforcement Learning skills help with data analysis, scenario modeling, and predicting where new attacks will come from.
- Natural language processing can be used to mine text data sources, improve threat intelligence, and add to knowledge resources.
- Automation can help organise tasks that take a lot of time, speed up response times, and lighten the load on human analysts.
As AI and automation are becoming increasingly important to protect a growing attack surface and deal with a huge rise in security events, one must wonder, what makes them work so well? With the help of AI-generated insights, automation tools that are driven by AI can identify threats by user, device, or location and then take the right steps to notify and escalate them while human experts decide how to investigate and fix the problem. When these capabilities are in place, cybersecurity analysts can focus on solving more complicated problems that require human judgment.
Some of the universal benefits of coupling AI and automation include:
- Less time needed to find, respond to, and get over incidents
- Improved governance and compliance for security.
- Less analyst’s tiredness, helping them make better, more informed decisions faster and with fewer mistakes.
- Reduced costs of cybersecurity by at least 15%, highlighting that processes for protection, prevention, detection, and response are more efficient and productive as a whole.
- Cuts to data breach costs by at least 18%, which shows that the detection and response processes are working better.
- Increased return on security investment (ROSI) by 40% or more.
Additionally, implementing automated and AI-driven edge security at the point of access will protect against zero-day threats, malware, and other vulnerabilities. When pairing AI with automation, companies gain better performance, whether in terms of speed, insights, flexibility, or scalability.
WeForum research suggests 48% of organisations believe automation and machine learning will introduce the biggest transformation in cyber security in the next two years. This is particularly important as studies find that by 2025, cybercrime will cost the world economy an average of $10.5 trillion each year. And, according to the annual Ponemon Institute and IBM Cost of a Data Breach report, the best way to reduce the overall costs of a data breach was to use AI and automation together.
Behind the scenes, algorithms that use AI, deep reinforcement learning, and machine learning look at a huge number of examples to find patterns and learn how to respond best to different factors. This training is a key part of making AI models work better.
Using AI and automation to improve model accuracy helps analysts avoid alert fatigue by differentiating between real security threats and everyday events. Security teams can then prioritise security events, gain more information about the security event context, and then help them inspect and investigate. Also, by using AI to improve the signal-to-noise ratio, analysts can spend their time on real threats that pose the most risk.
Let’s not forget that AI can look at both structured and unstructured data sources, combining internal and external data with threat intelligence services, offering a full picture of the situation and the threats that are present. For people who work in cybersecurity, this cuts down on how long it takes to find, respond to, and get over an incident.
AI and automation help improve security governance and compliance by making it easier for procedures to be escalated, reviewed, and fixed. By automating AI, analysts who do repetitive, time-consuming tasks are not overwhelmed with exhaustive tasks and help them make better, more informed decisions faster and with fewer mistakes.
Security AI and automation solutions reinforce resilience, which is essential to defending an expanding attack surface and responding to the huge increase in security events, impossible for humans to keep up. In fact, companies that realise the increasing cyber threats and actively take steps towards strategizing on how to win with AI and automation ensure fundamental improvement in the performance and effectiveness of the security functions.