AI in Cybersecurity: What’s Out There and What Technuf Has to Offer

Cybersecurity has a lot to do with thinking like the bad guys while being the protagonists. Now that artificial intelligence (AI) has reached everyone, including bad actors, it’s imperative for organizations to arm themselves to protect their digital assets against AI-powered threats with the power of AI. This article explores the different AI-related issues and solutions to defend against cyber threats. Also, learn how Technuf stays ahead of the game and can safeguard your organization.

Every person and their dogs have heard of Artificial intelligence (AI) today thanks to the likes of ChatGPT, DeepMind, and even Siri. Its impact on cybersecurity comes as a surprise to no one. While there was some mystique surrounding AI, particularly in sci-fi movies and novels, now people are both enthralled and concerned when coming face to face with it. Many are curious about the role that AI will play and has played in cybersecurity because of the rise of AI automation and machine learning (ML). In this blog post, we will dive into the impacts of the marriage between cybersecurity and AI, past AI-related incidences that changed the cybersecurity landscape, and what Technuf brings to the table to safeguard enterprises against AI-powered cyber treats, utilizing the power of AI.

What is AI, and how does it play into Cybersecurity?

AI has been classically used to mimic human-like decision-making when it comes to searching, be it a search engine like Google or solving an equation. However, in the contemporary world, AI has become synonymous with Machine Learning, which is where it gets interesting.

Modern AI (including generative AI like ChatGPT or Dall-E that are all the rage today) is intelligent software systems that learn from examples. Let’s use an elementary toy example to get the point across.

Assume you show the AI five pictures of apples and five oranges. Now, you introduce a new picture of an apple or orange and ask the system, “What fruit is it?” If trained well, it will be able to distinguish an apple from an orange and generate the correct answer. It is very similar to human cognition. Succinctly put, modern AI systems learn from examples just like a human child.

This simple philosophy carries through in today’s most advanced AI systems, even though the principle was born in the 1960s with the advent of a perceptron to imitate human learning.

How is this relevant to cybersecurity – you ask? Simple. Instead of apples and oranges, consider safe and malicious traffic to your organization’s web portal. And there you have it! That is precisely how AI manifests itself in cybersecurity – both on the good and bad sides.

Areas Where AI Plays a Pivotal Role in Cybersecurity

AI can play both the hero and the villain based on who uses it and how. Being proponents from the preventive side, let’s dive into some areas where AI has proven extremely effective in safeguarding cyber systems.

Threat Detection and Prevention

The ability of AI to swiftly and accurately evaluate massive amounts of data is one of the technology’s most important effects. Traditional cybersecurity techniques are largely insufficient in light of the sophistication of self-securing cyber-attacks. Compared to humans, AI can identify threats and act faster, and it can also learn from past episodes to enhance its detection and prevention abilities. Its real-time threat detection and response capacity is another advantage in cybersecurity. AI algorithms can continuously monitor networks and systems for anomalies or suspicious activity while evolving their capabilities from the data they see. It can act rapidly when a possible threat is identified, stopping the attack or isolating the harmed system to limit additional damage.

Assessment and Management of Vulnerabilities

A system or network’s vulnerabilities can be detected using AI, which can then be ranked according to their seriousness. This can assist security teams in concentrating their initial efforts on the most severe vulnerabilities. Through the recommendation of patches and upgrades to address discovered vulnerabilities, AI can also help with vulnerability management.

Incidence Response

AI can be used to accelerate incidence response operations in the case of a cyberattack. AI algorithms can assess an attack’s extent and seriousness and recommend the best course of action to reduce damage and stop additional compromise. They can automate many incident response functions, such as isolating compromised computers containing malware and blocking harmful traffic.

Threat Analytics and Intelligence

Evil never sleeps. To find new risks and malicious patterns, AI can be used to examine enormous amounts of threat intelligence data. It can discover new malware strains, vulnerabilities, and attack methods by scanning social media, internet forums, and other sources. This can assist security personnel in staying on top of things and actively defending against new threats.

Addressing Zero-day Vulnerabilities

Security flaws known as zero-day vulnerabilities are those that the general public or the software vendor is not yet aware of. Because attackers may use these vulnerabilities to their advantage before a fix is made available, they pose a grave risk. By examining software code and behavior patterns, AI can be used to find and classify zero-day vulnerabilities. For instance, MIT researchers created a system that uses AI to detect and fix zero-day vulnerabilities automatically.

How significant are the looming threats?

AI-powered attacks and defensive mechanisms are not a thing of the past; they are here and now. In the past decade, a myriad of incidences has occurred worldwide where AI and cybersecurity met – either in harmony or discord. Let’s walk through some interesting chronicles of the new wave of security and threats.

The SolarWinds attack is perhaps the most well-known AI-powered attack on US soil. The renowned IT management company SolarWinds’ software supply chain was penetrated by these attacks, which were detected in 2020. Upper echelons of the government, like the Treasury Department, and Fortune 500 companies, like Microsoft and Cisco, were impacted. Security professionals found locating and responding to the attack challenging because the attackers utilized AI to blend in with regular network traffic and avoid detection. It led to an estimated loss of $90 bn. This had massive geopolitical implications, including foreign sanctions.

More recently, the takedown of the Emotet Botnet was significantly impactful. Emotet was one of the world’s biggest and most deadly botnets and was eliminated in 2021 by a global law enforcement effort. It penetrated over $1.5 million machines and caused an estimated damage of $2.5 bn. By examining the code of Emotet to find new versions and foresee where the botnet’s controllers could strike next, AI played a significant part in the operation.

Besides these, there are many incidences where AI and cybersecurity intersected, like the NotPetya attacks and Marriott Scams.

What about the good guys?

During these periods, some insightful firms took preemptive actions. It was time for the sentinels of software systems to arm themselves.

The most notable one was IBM’s Deeplocker. Researchers at IBM created a proof-of-concept AI-powered malware called DeepLocker in 2018. This malware used AI to evade detection by only activating when recognizing a specific target, such as a particular face or voice. This makes it difficult for traditional solutions to detect and prevent. And naturally, they also implemented the cure using AI as well.

More recently, Cybereason, a cybersecurity firm, debuted an AI-powered threat-hunting tool in 2021 that employs AII to find and address sophisticated cyber threats. This system examines data from endpoints, network traffic, and other sources to spot potential risks and give security professionals actionable items to manage them.

With a trail of knowledge and background, today, Technuf is equipped with AI-powered cybersecurity solutions for mission-critical enterprise solutions.

What Technuf Has to Offer

Having worked with a plethora of enterprises, including the top-most echelon of the government and Fortune 500 companies, Technuf has a uniquely advantageous position when it comes to data availability, which is the key to robust AI models in cybersecurity. As such, Technuf has integrated AI and Machine Learning into several of its solutions. The company acknowledges the paradigm shift in assessing, managing, and containing threats. Consequently, it has embedded AI-powered features in its solutions in the following ways supporting multiple products deployed in top governmental agencies and enterprises.

User Behavior Analytics

In cybersecurity, we humans are the weakest link, and one negligent button click can wreak havoc on your company. To learn more, read our blog post – Cyber Security: It All Comes Down to the Human Factor. Acknowledging this phenomenon, Technuf can use its large user-data repository to learn and model these behaviors for state-of-the-art threat detection using AI. What seemed impossible even half a decade ago, Technuf can do by letting the AI model analyze the usage patterns, behaviors, and user credentials, among other attributes, to deliver top-of-the-line security intelligence.

Predictive Analytics in Security Incident and Threat Management

Technuf supports several large-scale security systems already deployed and safeguarding governmental agencies. One notable solution is Splunk, a security incident and event management (SIEM) solution. Technuf is supporting Splunk to expand to perform compliance and threat management. Furthermore, with the massive streams of data at its disposal, Technuf is working with multiple entities to implement counterintelligence and threat analysis features powered by AI. This includes ML modules embedded in the analytics engine to detect anomalous and suspicious user behaviors. Therefore, Technuf is at the forefront of AI-powered threat management solutions.

Network Trend Analytics for Threat Intelligence

All companies’ security threats should be detected and exterminated at their network’s edge before they can penetrate the organization’s domain. This is where Technuf steps in with its AI-based tools for network trend analysis to detect security threats and anomalies in traffic. This also pertains to having access to big data warehouses, which Technuf uniquely possesses thanks to long-term experience in cybersecurity. Therefore, using predictive AI tools trained on massive amounts of network data, Technuf can assist in detecting and preventing network traffic anomalies and breaches. One such solution Technuf supports is Microsoft’s M365 Sentry, which offers AI and ML-based network security features for several enterprises’ geofencing and compliance scans, to name a few features.

Concluding Remarks and Pathway to the Future

In conclusion, Technuf acknowledges that the AI revolution is a highly fast-paced and dynamic phenomenon. Staying on top of it is imperative because the dark side is not sitting this out. Therefore, Technuf suggests that the best way organizations can protect themselves from the looming yet inevitable threats is to adapt and embrace data-driven security protocols that are growing every moment. This does not necessarily mean new software solutions from scratch but critical patches and AI features – powered and trained by more robust data. Therefore, as a company, Technuf is already working on adding newer and better AI-powered solutions owing to its unique data advantage on top of its existing AI and ML-based security solutions.