Like it or not, cybercriminals are always on the move, plotting nefarious plots and experimenting with new tactics. But that doesn’t mean founders and CTOs are defenseless. With AI in cybersecurity, you can better improve your security posture and be more proactive when responding to known and unknown threats.
Startups and SMBs share a common concern as they strive for growth while they balance going digital and staying safe. Over the years, we have heard about incidents where data breaches, ransomware, and other security incidents have compromised large organizations. For example, NHS London got hit by ransomware in 2024, and 400 GB of sensitive data was leaked online. But what is equally worrying are cybersecurity incidents that affect smaller businesses.
But how do we go about that? What are the AI technologies that you can use to safeguard digital assets?
I’m Oleh Komenchuk, an AI & ML Engineer at Uptech. Over the years, I’ve helped small businesses worldwide incorporate AI technologies in their products. In this guide, I’ll explain ways where you can apply AI as part of your cybersecurity strategies.
What is AI in Cybersecurity? And What Businesses Are High-Value Targets?
AI in cybersecurity describes how computer security specialists leverage artificial intelligence (AI) to detect, analyze and respond to digital threats. AI excels in detecting complex patterns, anomalies, and behavioral changes. Depending on the AI algorithm deployed, security teams can reduce risks of oversights, security blindspots, or false negatives that subject digital assets to attacks.
That said, AI adoption in cybersecurity is still in its infancy. According to a survey, only 18% of organizations fully deployed AI for assessing cybersecurity risks and mitigative measures. On top of that, 70% reported that AI is highly effective in detecting unknown threats.
The notion of AI in cybersecurity isn’t new, but the way it is applied has evolved in tandem with AI technologies. Rather than basic rules-matching detection, cybersecurity AI now uses state-of-the-art technologies like machine learning (ML), natural language processing, and behavioral analytics.
Because the attack surface has changed drastically in recent years, AI’s role in cybersecurity has become more important in addressing common threats affecting SMBs, including:
- Ransomware
- Phishing
- DDoS attacks.
- Virus/malware.
- Insider threats.
According to Statista, there were 317 million ransomware attempts in 2023 worldwide. If we take a closer look, the number of ransomware attempts actually decreased significantly between the third and fourth quarters, dropping from around 155 million to nearly 102 million cases.
Another noteworthy point is that, in the event of an attack, many organizations prefer paying the ransom to restore stolen data rather than reporting the incident immediately. This approach may stem from a desire to hide the breach, as making it public could damage the company’s reputation.
Left unaddressed, cybersecurity breaches can be very costly for SMBs. Small businesses lost an average of $255K per incident, while some paid up to $7 million.
Taking a broader view from March 2022 through February 2024, the healthcare industry recorded the highest average cost of a data breach, reaching nearly $9.7 million. The financial sector followed, with breaches costing an average of $6 million each. Over the same period, the global average cost of a data breach was $4.88 million.
To make things worse, SMBs are attractive targets for cybercriminals. Many SMBs still apply traditional security measures, which fail to protect a more complex attack surface effectively. On top of that, smaller businesses lack the funds that larger enterprises could afford to strengthen their security measures.
How AI Can Be Used In Cybersecurity?
We know that protecting your systems can be frustrating. Somehow, cybersecurity has turned into a cat-and-mouse game between hackers and security specialists. So, it isn’t surprising that you’re seeking ways to gain the upper hand. In fact, many of our clients face similar problems when they come to us.
To help you boost cybersecurity with AI, we share several tactics that you can apply in your company.
1. AI-powered security platforms
Chances are, you’re using cloud security platforms to detect, block, isolate, and mitigate incoming threats. Previously, such platforms used signature comparison to detect malware, trojans, viruses, and other harmful programs.
However, rapidly evolving threat agents and complex security requirements render conventional measures obsolete. These days, threat actors can target a connected device, such as a mobile phone, by circumventing traditional firewalls safeguarding centralized servers. And this calls for a more holistic and intelligent security platform.
An AI-powered security solution can offer robust protection and prompt responses that businesses need. Whether you want to protect servers, workstations, or mobile devices, AI, or specifically generative AI, holds the answer. With AI, security platforms can consolidate data from various sources, allowing oversight of all digital assets. Besides securing data, here are what opportunities generative AI brings when you implement it in your business.
Here are some examples of such AI-powered companies that combine both AI and cybersecurity: Crowdstike, Tessian, Fortinet, Drata, and Darktrace.
2. AI in incident management
Another area where AI in cybersecurity can be helpful is in assisting security teams in responding to possible system breaches. Conventionally, SMBs rely on manual or semi-automated approaches to detect, analyze, and resolve data incidents, which can cause substantial response delays.
AI greatly shortens the period where you first detect anomalies, such as a breach attempt, to deploy mitigative measures for securing your assets. By using machine algorithms, cybersecurity software can monitor and pick up anomalies in real time. Basically, you’ll know someone is trying to access your system before they had the chance to do so.
Besides prompt remediation, an AI-powered cybersecurity mechanism can also ensure that you respond to threats in ways that comply with stipulated regulations. For example, companies operating in the EU are subject to the NIS 2 directive, which requires organizations to detect, mitigate, and report vulnerabilities and threats impacting their system.
3. AI in fraud detection
Although it doesn’t directly compromise data security, online fraud can cost startups and SMBs dearly. Such attempts, when successful, result in financial and reputational losses. In the US, 880,000 people fell victim to online fraud in 2023, losing $12.5 billion dollars in total.
As a founder or CTO, the responsibility to prevent your customers from falling victim partly lies on your shoulders. Again, artificial intelligence plays a great role in this. As we’ve mentioned, generative AI is exceptionally adept at detecting complex patterns.
Hence, we can deploy an AI-powered cybersecurity solution to continuously monitor transactional data and raise alerts when it detects anomalies. The mechanism can halt an account to allow manual intervention and prevent financial losses. For example, JP Morgan incorporates AI-based fraud detection into its payment system and has since observed fewer fraud cases.
4. Securing remote work environments
Ever since the pandemic hit, remote work has become the new norm. While employees enjoy the flexibility of working in cafes and co-working spaces, they might be unaware of the risks of being connected to unsecured public WiFi.
Unlike an encrypted corporate network, bad actors can potentially intercept data shared over public networks. And this poses security challenges to startups and SMBs. To ensure security resilience, you’ll need a robust, flexible, and scalable cybersecurity measure.
Cloud-based AI-powered cybersecurity software can monitor suspicious activities, block specific connections, apply software patches, and more in real time. This way, remote workers can continue using their laptops and portable devices without becoming the weakest links that bad actors could target.
5. AI-powered security automation
The first step to safeguarding sensitive data is to detect vulnerabilities in a timely manner. But to truly improve security posture, you must ensure that subsequent steps are also carried out promptly. Otherwise, perpetrators can exploit delays between vulnerability discovery and resolution.
Let’s say a company discovers a vulnerable flaw in one of the applications deployed in their system. But because of the delay in patching, which might take up to 3 weeks, the entire digital infrastructure remains vulnerable. However, with AI, the time gap can be greatly reduced, which minimizes the risk of a potential exploit.
By integrating AI with cybersecurity solutions, business owners can automate routine security tasks. For example, you can schedule patch management to ensure all software applications are safe from zero-day exploits. Furthermore, cybersecurity AI can preempt attacks by predicting adversarial behaviors based on past data.
6. AI-powered network monitoring
As businesses shift their data and workload online, they need secure, reliable, and scalable network infrastructure. However, network topology has become more complicated. IT administrators must now deal with cloud servers, private data storage, workstations, and devices distributed across different locations.
Network monitoring, admittedly, has become an arduous task for conventional means. But with AI network security, administrators can confidently pre-empt adversarial threats, human errors, and other causes of downtime. AI technologies like machine learning and generative models can spot anomalies that often escape human oversight.
Let’s take Toyota as an example. The automotive giant has deployed an AI-powered cloud monitoring at its US operating subsidiary. Before that, engineering teams face challenges when troubleshooting issues at their manufacturing plants, mainly because of the distributed architecture. However, with AI, they managed to decrease the mean time to detection (MTTD) from 6 hours to 15 minutes.
7. AI in cybersecurity training and simulation
Cybercriminals have resorted to more creative ways of attacking, and presumably, many of them are turning to AI. Companies must find ways to upskill their security teams to prevent being taken by surprise. On this note, cyber AI can help.
Cybersecurity artificial intelligence technologies, such as machine learning and predictive analytics, can help security teams simulate adversarial attacks to test existing defenses. They can also analyze historical data and current trends to predict when the next attack will likely happen and take precautionary measures.
Besides improving cybersecurity preparedness, AI can also help increase cybersecurity awareness among employees. Every individual has a different level of understanding of how they can better protect their data and privacy. With AI, your company can personalize cybersecurity learning materials to help employees learn more effectively.
8. Behavioral analytics for insider threat detection
Often, conversations on cybersecurity focus on blocking external threats from penetrating layers of security. However, some threats originate from within the company. According to a study, 83% of companies reported at least one insider threat in 2024.
As most security solutions are designed to guard against external threats, they are ineffective against, let’s say, a rouge employee trying to access an administrative account. To mitigate them, we use AI-powered behavioral analytics. The technology can detect suspicious activities that might point to an adversarial action from within the security perimeter.
For example, AI-powered cybersecurity software monitors employee activities in real time and compares parameters like access time, duration, and downloads. If the software finds an employee makes several huge downloads, which is deemed out of the ordinary, it will immediately alert the security team.
4 Best Practices for Adopting AI in Cybersecurity
You’ve seen the potential of AI in enhancing cybersecurity. The question is — how do you adopt it in your startup or small business? Because each business is unique, you’ll need to explore possible ways to get the best out of AI in cybersecurity. Below, we share steps that you might find helpful.
Assess your cybersecurity needs
If you’re a startup, you don’t need enterprise cybersecurity solutions that large organizations use. Likewise, if you’re running an SMB, you will likely have your own cybersecurity concerns. What’s important is to audit your existing cybersecurity infrastructure and the risks your business is exposed to.
For example, if you lead a fully remote team, then you might be worried about unpatched vulnerabilities in your employee’s devices. Meanwhile, an e-commerce business could be worried about a DDoS attack targeting their server or database.
So, run a full cybersecurity audit to find out your immediate needs. If you need help, do reach out to our team. We provide consultancy services, and we can help you assess the cybersecurity solutions your business needs.
Find a trusted AI partner
If you want to adopt AI as part of your cybersecurity strategy, you’ll need to engage artificial intelligence cybersecurity experts. Otherwise, you might be overwhelmed by the technical complexities of training, fine-tuning, testing, and integrating AI models with existing cybersecurity solutions.
Generally, there are two ways to bring AI experts to your team:
- by hiring in-house
- or outsourcing.
If you hire in-house, you get complete control, focus, and dedication from the AI specialists. However, you’ll need to commit to their salaries, perks, training, and other long-term employment needs.
On the other hand, outsourcing lets you be more flexible when budgeting for operational expenses while accessing capable AI talents. And if you’re based in expensive cities, outsourcing to countries like Ukraine can also help you reduce costs.
For example, companies like Hamlet, Angler AI, and Tired Banker outsourced their AI development to Uptech. They trust our team’s deep AI expertise and the experience we had in building secure and compliant apps.
Hamlet is an AI-powered text summarizer. We implemented the tech-davenci-003 model within a web app, architected a scalable infrastructure and incorporated UI/UX features to provide an intuitive user experience.
Angler AI is an AI-powered platform that helps brands significantly improve customer acquisition and lifetime value with AI-powered ad campaigns.
Tired Banker is a web app that turns complex financial reports into easy-to-understand information. When taking on the project, we had to balance a user-friendly interface with state-of-the-art AI technologies. To do that, we integrated the app with GPT while creating an engaging layout inspired by the nostalgic 90s.
Learn more about our AI development capabilities here.
Choose the right AI solutions
Next, you’ll either procure off-the-shelf AI-powered security solutions or develop a customized one. Either way, it’s important to know what to look for when searching for cybersecurity software. For example, if your team largely works from home, you’ll need solutions that can quickly detect and respond to threats on a complex attack surface.
Meanwhile, if you’re already using multiple cybersecurity software from different vendors, integrating them with AI is a great idea. This way, you can have a unified overview of device statuses, software vulnerabilities, and incident responses.
Besides deciding on the AI technologies, you’ll also need to consider the cybersecurity partner you’ll collaborate with. While many might boast promising portfolios, look deeper into how clients felt after working with them. Also, remember, the cheapest vendor is seldom the best.
Build a comprehensive security framework
Your effort to integrate AI with existing security systems must align with the objectives and security gaps identified earlier. Whether you want to reduce mitigation response time, improve data protection, or decrease workload, communicate your objective with the AI developer.
Then, inform all stakeholders of the security updates and gradually implement them. Be mindful of the fact that AI, despite the advantages it provides, is not perfect. Therefore, always ensure that your IT team is continuously monitoring the system’s performance and making subsequent adjustments if necessary.
AI Technologies in Cybersecurity
When we develop AI-powered cybersecurity solutions, we use one or more types of AI technologies.
Machine Learning (ML)
Machine learning is a software algorithm designed to learn and emulate a human’s decision-making process. AI experts train a machine learning model with datasets before they integrate it with cybersecurity products. Through training, the ML model can predict and reason to a certain degree without human intervention.
In cybersecurity, machine learning is helpful for behavioral analytics. You can use it to analyze and identify abnormal events, such as irregular login times or an uptick in downloads.
Large Language Models (LLMs)
Large language models AI technologies that can understand and respond like how people do. Examples are GPT, Gemini, and Llama. They excel at recognizing patterns in raw data, thanks to extensive training with massive datasets.
Since making its entrance to the public, cybersecurity specialists have explored LLM’s potential in certain fields, including threat detection and responses. An LLM-powered cybersecurity solution can quickly analyze security log files, network activities, emails, and other textual data at scale to identify threats.
Deep Learning
Deep learning is a more advanced version of machine learning algorithms. It comprises several hidden layers of neural networks, which allows it to be more accurate when predicting possible outcomes. Deep learning models are also self-adaptive, which means they can learn and improve their reasoning capabilities while interacting with data in real time.
Because of their ability to process data at scale, deep learning models are helpful in analyzing threat signals in complex network setups. For example, you can use deep learning powered anti-malware to detect emerging threats that were yet to be cataloged in the signature database.
How to mitigate the risks of relying on AI for cybersecurity?
AI, especially generative AI, has tremendous potential in boosting cybersecurity. That said, the technology isn’t without risks. Thankfully, if you know what the risks are, you can mitigate possible downsides and turn AI into a useful component to safeguard your digital assets.
Recognize the risks
First, you’ll need to come to terms with AI not being perfect. Generative AI models are like black boxes, which causes concerns about transparency and accuracy when predicting outcomes. Also, AI models are influenced by the datasets on which they are trained. If the datasets are biased, the models may not be fair in their results.
Another concern is privacy, particularly when you train machine learning models with sensitive data. Without security measures, such as encryption, there are risks of data breaches, whether by force or accidental.
Balance automation and human expertise
It’s tempting to let AI run all the show when it comes to automating cybersecurity tasks. But let’s be mindful of AI’s limitations. As helpful as AI is, there are times when it may predict inaccurately. For example, AI might miss out on threat signals that experienced human cybersecurity professionals would pick up easily.
So, adopt a more balanced approach when you integrate AI with your company’s cybersecurity workflow. Upskill your security team so they can better manage and respond to complex scenarios with AI cybersecurity tools.
Address regulatory and compliance
Whether you want to train or deploy AI cybersecurity software, you’ll need to meet the compliance requirements in the industry or region you operate in. For example:
- Medical companies must comply with HIPAA, which sets rules on keeping health data safe.
- In the EU, GDPR, makes businesses explain how they gather, store, and use data.
- Firms in California must obey CCPA, a privacy rule like GDPR.
Compliance aside, companies are also expected to navigate ethical concerns, such as demographic bias, when applying AI to their products.
Can AI replace human cybersecurity experts?
AI has come a long way from the days when it was limited to simple rule-based systems. Today, AI can help humans solve complex problems, exhibit confident reasoning capabilities, and automate cybersecurity workflows. Yet, AI cannot replace us. And that holds true even for generative AI.
While genAI excels in processing massive data, it lacks creative thinking, emotional intelligence, and innovative ways of solving problems that humans do. Rather than consider AI as a threat or a replacement, which both aren’t contributing positively to cybersecurity, think of AI as an adept assistant.
Use AI to augment cybersecurity efforts in ways that reduce operational costs while increasing digital resilience.
Talk to Uptech now to protect your business with cybersecurity AI.
FAQs
What are the primary benefits of AI in cybersecurity for SMBs?
The immediate benefits are faster threat detection and response, even when SMBs operate in a very complex network setup. For example, AI can monitor and secure servers, workstations, and remote devices for startups with a hybrid workforce.
Why is AI considered better than traditional cybersecurity methods?
AI is faster and more robust than conventional methods. With technologies like machine learning and predictive analytics, AI in cybersecurity can detect emerging threats and prevent zero-day attacks. Meanwhile, rule-based cybersecurity software is limited by signature-based comparison, which is slower to block potential threats.
How does AI detect threats in real time?
AI cybersecurity tools monitor threat signals continuously, picking up anomalies that might indicate adversarial activities. For example, when you deploy an AI-powered threat detection system, it scans IP addresses, network logs, and other data sources in real-time to identify potential threats.
Can AI protect against insider threats?
Yes. AI can discover suspicious activities originating from within an organization, such as log-in at odd hours, unusually large downloads, and repeated sign-in attempts. Then, the AI-powered software will alert security personnel and take appropriate measures to prevent additional damage.
How does AI help prevent phishing attacks?
AI, along with its NLP capabilities, can analyze emails and employee behaviors when responding to them. For example, if an employee clicks on a suspicious link or navigates to malicious websites, AI can block such interactions to prevent potential fallouts.
How does AI help manage compliance with regulations like GDPR or HIPAA?
Many regulations require organizations to discover, respond to, and report threats and vulnerabilities promptly. AI does that and more. With AI, businesses can monitor data security, compile comprehensive reports, and facilitate compliance audits without excessive human intervention.
How do I choose the right AI cybersecurity tool for my SMB?
First, you’ll need to identify your cybersecurity needs. Then, decide what the immediate priority is. Some businesses need help with advanced threat detection, while others may want to unify disparate security protocols with an AI platform. Once you know what you need, look for the right AI vendor to develop the security solution.