Artificial Intelligence and Cybersecurity: Attacking and Defending

Artificial Intelligence (AI) is changing everything about cybersecurity for better and for worse.

But first, what is AI?

Artificial Intelligence (AI) is a branch of computer science that attempts to replicate or simulate human intelligence in a machine, so that it can perform tasks that typically require human reasoning. Some programmable functions of AI systems include planning, learning, reasoning, problem-solving, and decision making, resulting in AI being able to learn on its own and adapt , grow and change based on new data.

AI, like any computer, has the super-power of data-processing, but in addition to standard computers, it has the evolving ability to analyze large volumes of data and identify, isolate and explain patterns. It can also predict trends and particular behaviors.

AI presents opportunities for cybersecurity professionals to improve their cyber defenses and opens new grounds for threats as cyber attackers leverage publicly available machine learning (ML) algorithms.

Using AI to enhance cybersecurity

Cyberattacks are growing in volume and complexity in a digital age where time has become a fundamental issue.
In this complex context, AI allows under-resourced security operations analysts to prevent threats by curating threat intelligence from millions of published research papers, news stories, and blogs.
Theoretically, AI provides instantaneous insights to filter through the noise of thousands of daily alerts, decreasing response times drastically- by cutting the human error and time spent to recognize and isolate a cyberattack.

The use of AI is meant to prevent an attack rather than remedy to it

The fact is that cybersecurity products can only really detect malware or suspicious incidents if they know what they are looking for.
The problem is that cybercriminals always find new ways to perform attacks and develop new forms of malware, implying that someone has to detect the breach before a cybersecurity product or an antivirus is being updated to it.
Where AI and ML come in handy is that, by analyzing all types of malware that are around and self-learning what is potentially bad, it will be able to recognize in the future similar patterns before they have even been made known to the wide public.
In theory, an AI-based tool could recognize a malware which has been adapted to avoid detection from standard cybersecurity tools and would go under their radar.
But uncovering new kinds of malware isn’t the only way machine learning is being exploited to boost cybersecurity: an AI-based network-monitoring tool could as well track what users do on a daily basis, building a profile of their typical behavior pattern.
By analyzing this information, the AI can detect anomalies and react accordingly.

Machine learning is a powerful concept that allows training AI to take specific actions based on known patterns, without having to code the references their behavior will be based on.

By using Machine Learning, AI has the ability to adapt and respond to a constantly changing world. AI ultimately enables us to understand the relevance and potential consequences of a breach or a change of behavior in real-time, and simultaneously develop a proportionate response.
For example, if an employee clicks on a phishing link, the system can spot almost immediately that this qualifies as abnormal behavior, which could potentially be an opening for a malicious intrusion, such as malware being deployed or gaining access to the network: therefore, the AI will engage a “blocking” protocol. The action taken by the AI of blocking such suspicious action is proportional and taking split seconds, making it the perfect tool in companies where speed is of the essence and being connected is key: it can really help brands protect themselves from cyberattacks without disrupting the ongoing work of the organization since, if the potential malicious behavior is spotted on only one machine, it won’t result in shutting down the whole network.

AI systems are generally making deductions and decisions in an automated way without any human involvement or input.
The reasons why an AI program makes particular deductions and decisions are not always intuitively clear or transparent and quickly interpretable by its overseers; this means that even if a violation is detected, its purpose can remain opaque while the AI will launch safety protocols.

Where AI and machine learning can get you into trouble is when you assume that AI and ML are going to solve all your problems.

This is where malicious actors take advantage of the machine learning process and taint the data pool from which these systems learn how to identify malicious code. By inserting fraudulent code into the process, attackers can cause a system to generate false positives, undermining its intended functionality.

Feeding these complex automation to make the process of learning effective for future independent actions, the quality of the inputs is crucial, so make sure to feed your AI-based system with clean and legitimate data. If the model hasn’t been properly tuned or adjusted, and the learning part has been based on wrong, inaccurate or outdated data, it may then allow in the future real threats to pass through: that’s the worst-case because while thinking your organization is fully protected by AI, you are in fact fully exposed. It could be easily compared to a physician making a diagnosis using compromised, inaccurate, or altered medical records… imagine the damage!

For all those reasons, AI-based cybersecurity is in no way a substitute for human security staff.

AI used to power cyberattacks

Artificial Intelligence is no longer a tool only for the “good guys”; malicious actors now use it as a force multiplier as well.

This new era of offensive AI leverages various forms of machine learning to supercharge cyberattacks, resulting in unpredictable, contextualized, speedier, and stealthier assaults that can cripple unprotected organizations- Forrester Consulting,

While AI tools help to defend against hackers, hackers themselves are using the same techniques to improve their attack methodology.
Cyberattacks are becoming extremely common, and have been acknowledged as one of the most strategically significant risks facing the world today.
Nowadays, digital assaults against governments, critical infrastructure, large and small corporations, and organizations are a common threat.
No sector is immune from cyberattacks, the level of sophistication of the threats is continually increasing, and their aim is not only to steal data but also often to manipulate or change it.
AI’s ability to adapt and learn will lead to a new era in which highly-customized human-mimicking attacks will be scalable.

Artificial Intelligence (AI) and the Internet of Things (IoT)

IoT is the network of physical objects: it involves adding internet connectivity to system devices, mechanical and digital machines, objects, animals, and/or people.
These “things” that can be implanted with technologies, sensors, or software that further helps in connecting or exchanging data with other devices or systems via the internet or vice versa.

AI and IoT are going hand in hand: as while IoT is transforming the way we learn by altering the way we live, AI is learning the new pattern of the way we live and building on it a predictive pattern making it the new conventional standard pattern.
The entire idea of AI is to gather and record more actionable data; IoT devices are providing an effective and accurate means of data gathering.

It is forecasted that by 2025, there will be approximately 44 billion IoT devices worldwide,

How can IoT (Internet of Things) expose your Hotel?

Smart speakers, smart lights, smart locks, and thermostats, are only a very little sample of IoT installed in your facility. Many of these “gadgets” are not even secure, they have nowhere near the same number of safeguards you can find in smartphones and computers.
Hackers can easily use smart devices embedded in any hotel room as entry points and get access to the hotel’s most sensitive data stored on reservation systems and point-of-sale.

It is important to keep in mind that this kind of attack can come from both people at the property and people attacking remotely; therefore hotels’ systems need to be constantly monitored while making sure that networks are built with barriers between different systems and networks, with firewalls.

The mobile integration of IoT devices facilitates the gathering of data about your guest’s preferences (with their permission) allowing you to customize their future reservation based on previous stays.

For hotel guests, the availability of IoT devices in their accommodations makes for a more comfortable and customized stay; but for the hotel, it means more connective points to the internet — synonym to potential breaches and vulnerability to Cyber-attacks. Connecting IoT devices to your IT network extends the attack entry point for hackers to exploit, making your all system network vulnerable to the malicious intends of skilled cybercriminals

Conclusion:

The reality in cybersecurity is that AI tooling and capabilities are now in the hands of both the good and the bad sides of applied AI.
AI-based security solutions should be implemented as early as possible and teams trained to feed those AI through accurate ML, with reliable data and effective supervision. Partnering with a professionally trained workforce such as an expert consulting company is the most efficient way to guarantee accuracy and effectiveness in keeping your client’s and your organization’s data safe.