Table of Contents
Introduction:
According to recent data, around a quarter of contemporary US enterprises use artificial intelligence (AI) in some capacity. Yes, this includes the behemoths, such as automakers and financial organisations. However, small and medium-sized firms are now included on the list of early adopters (SMBs).
Many departments of industry, such as marketing and customer service, are using artificial intelligence (AI) and machine learning to simplify and automate various procedures to make their regular job processes easier and smoother.
However, artificial intelligence has a significant impact on another critical industry: cyber security.
With the increasing availability of AI-powered IT security solutions, small and medium-sized enterprises can expect to have access to these advanced ways of threat detection and even prevention. In this article, we shall dive deep into ways artificial intelligence can be adopted to boost the cybersecurity of small businesses.
How can Artificial Intelligence boost the Cybersecurity of Small Businesses?
The ability of AI to detect and respond to the growing threat of cyberthreats is beneficial to an SMB’s cybersecurity.
With a success rate of 85-90 per cent, AI can accelerate this detection process by swiftly cross-referencing diverse security data and alarms. Using datasets provided by cybersecurity organisations, AI software may perform pattern recognition activities that assist it in detecting harmful behaviour within an SMB’s network faster and more quickly than a traditional cybersecurity system could.
Furthermore, an AI may be trained to study the micro-behaviours of ransomware attacks to detect the attack before the ransomware begins its encryption. When AI software discovers a potential network danger, it can minimise the workload for the cybersecurity analyst by prioritising the regions that require attention and automating the manual processes that the analyst normally performs. This allows the analyst to focus their efforts completely on studying and dealing with the system danger.
Most business owners are concerned about cybersecurity since a typical firewall can’t do much to prevent today’s outside attacks. The only way to keep control over new unknown hazards is to be proactive and transition from a traditional firewall to a smarter type of defence, such as deploying artificial intelligence (AI) solutions. Let’s understand below how artificial intelligence is changing the game of cybersecurity.
1. Spam Detection & Phishing Avoidance:
Businesses all over the world send and receive massive amounts of email every day, and spam filters are in place to safeguard inboxes from becoming inundated with spam and phishing efforts.
These filters use a type of regulated machine learning, which means that humans establish the settings and then the machines do the work to keep inboxes free of spam messages.
So, the next time you label a message in your inbox as spam, you’re training the machine learning filter to do a better job the following time.
2. Detection and Prevention of Incidents:
Intrusion detection systems (IDS) and intrusion prevention systems (IPS) are two key AI-powered cyber security tools that are becoming increasingly significant. And, thankfully, small and medium-sized firms are starting to have more access to them.
What exactly is an IDS?
An intrusion detection system (IDS) is a passive device that monitors a network or system for suspicious activities. Any incursion or violation of specified protocols will be scanned and archived for future analysis. They can also send an alarm to a system administrator.
What exactly are an IPS?
An intrusion prevention system (IPS) is installed in the direct path of network traffic. It will actively examine that pathway network to detect and prevent potential vulnerabilities, such as a person attempting to take control of a machine or programme.
The IPS can perform a variety of automated measures based on the threat it has detected:
- An administrator alert will be sent.
- Remove any harmful data.
- That source of traffic should be blocked.
- Reestablish the connection
3. Multifactor Authentication in a Dynamic Environment:
Not everyone in an organisation has access to all data; access privileges vary depending on the person, their job title, and even the location from which they are attempting to access data.
When AI is used with MFA, authentication becomes far more flexible and dynamic, with privileges adjusting in real-time. MFA with AI will collect user data and evaluate behaviour patterns to determine whether or not access to a specific system area should be given.
Another way AI can help us strengthen our online defences is by modernising a long-standing authentication process.
Most businesses have a security system in place to protect sensitive data, which includes an authentication system with several levels of access to protect sensitive data from both lower-level employees and outsiders.
However, this mechanism can be hacked. If someone with a high level of authentication accesses critical data remotely, an intruder can access it through a distant network. Conditional access is one way AI is attempting to address this.
A multi-factor authentication programme can be used by an AI system to construct a global authentication network with different access privileges based on geography, network, and individual authority.
The AI system will gather and evaluate user information within the network that is being accessed via this way. It will examine user behaviour, location, data transfer to and from the system, and the device used to submit the data. This information will subsequently be used by the AI to change that user’s access privileges, ensuring that sensitive data is safeguarded from intruders, even over long distances.
AI can prohibit intruders from accessing a user’s data on an SMB’s network in real-time by analysing the user’s data on the network.
4. Anti-malware protection:
Viruses, trojans, ransomware, and spyware are examples of intrusive software. It is frequently sent as a link or a file in an email and is designed to do significant damage to your company’s data and infrastructure.
The conventional ML approach relied on feature engineering to analyse and extract the malware program’s features, which were then compared to a default set to determine whether it was hazardous. However, this strategy ignores the fact that malware is always changing.
To combat these increasingly sophisticated malware threats, deep learning algorithms and large neural networks are being developed. These algorithms will examine and compare the dynamic components of a malware programme with its static properties to detect anomalies and block the malware.
Conclusion:
SMBs may now benefit from digital transformation. These new technologies are within reach and available right now, allowing you to compete and take on the major companies. SMBs who adopt emerging technologies like AI, like businesses, can gain a competitive advantage and grow smarter and quicker than the competition. We here at Security Pilgrim are here to guide and assist you in your endeavour of ensuring a cyber secure business.
