”With remote working, the cybersecurity threats for individuals and businesses have increased drastically, resulting in new technology and insurance solutions emerging to reduce the risk of cybercrime.”

With the pandemic and increased remote working, it's been a tumultuous 12 months for cybersecurity professionals.

Do you remember December 2020? Public reporting revealed details of a sophisticated supply chain attack against the update deployment mechanism of the SolarWinds Orion. The third-party software created a backdoor through which hackers could access and impersonate users and the account of each victim organization. The malware could also access system files and blend in with legitimate SolarWinds activity without detection, even by antivirus software. The hackers responsible used this operation to distribute and install malicious code, which spread across many organizations across multiple verticals worldwide.

Ransomware has grown to become a significant threat in recent years. Several high-profile attacks demonstrated to cybercriminals that ransomware was a profitable business. Such business in recent years was able to yield a rapid increase in cybercrime groups operating such type of malware. Indeed, on average, ransomware claims a new victim every ten seconds worldwide and costs businesses around $20 billion in 2020, increasing 75% over the previous year.

With remote working, the cybersecurity threats for individuals and businesses have increased drastically, resulting in new technology and insurance solutions emerging to reduce the risk of cybercrime.

Now, with artificial intelligence (AI) showing its power to help both catch bad guys and protect our data, we want to see improvements from the deployment of threat detection and risk mitigation solutions. In recent years, machine learning has become an indispensable tool to help companies detect malware by spotting patterns that would otherwise be hard to find.

I am thrilled to share below five critical considerations to help understand the linkages between the overall cybersecurity landscape, AI, machine learning, and the future of data breaches and other cybercrime tactics.

1. Machine learning is a subset of artificial intelligence that primarily automates threat detection algorithms.

Machine learning means AI that improves knowledge by consuming data from sources like blogs, news stories, digital footprints, and human behavior. Machine Learning can advance the field of cybercrime prevention quickly by developing artificial neural networks, which are pre-programmed to detect known patterns in malware code and varieties of cyber-attacks. Artificial Neural Networks mimic the way a human brain operates. Their training involves the consumption of billions of data artifacts from both structured and unstructured sources. The AI can then improve its knowledge of cybersecurity threats across both internal and external constituents, business units, and supply chains.

2. Cybersecurity teams are using machine learning to identify malicious behavior and stop attacks before they happen.

As noted, machine learning AI analyzes billions of data artifacts to identify key correlations and predict what could happen next. The more data is collected, the better artificial neural networks will detect cyber incidents and detect attacks before an incident occurs. The latter also includes artificial intelligence detecting patterns in code design by inspecting all aspects of code instances.

Following the WannaCry ransomware attack, which hit around 230,000 computers globally, one of the first companies affected was the Spanish mobile company, Telefónica. On May 12th, 2017, thousands of NHS hospitals and surgeries across the UK were affected too. Today, Telefónica and the NHS use artificial intelligence to automatically identify hacks and block them from infecting their systems to reduce malicious incidents or unknown threats that could cripple their entire organization and supply chains.

A vast volume of data artifacts containing billions of relevant data points is applied against the artificial neural networks. The more artificial neural networks train, the better they become at performing malware detection or preventing cyber-attacks based on well-articulated security strategies codified into security systems.

3. Machine learning is used for data analysis, predictive analytics, and anomaly detection.

Techniques such as machine learning and deep learning algorithms consistently improve their understanding of the changing cybersecurity landscape by detecting threats, deterring destructive phishing attacks, enhancing network security, and reducing the overall cybercrime risk an organization holds. By consuming billions of data artifacts, AI and machine learning algorithms analyze, predict, and detect anomalies, known patterns, and less well-established patterns in code before they occur. Approaches used to predict threats include security policies, behavioral analytics, biometric authentication, password protection, and intrusion detection, among others.

4. The future of cybersecurity involves more automation and AI-powered solutions to help protect against cyber threats.

In the future, AI and machine learning will detect special attacks before they happen and block them from infecting secured systems. They will prevent unexpected malicious incidents that could ruin an overall organization's ability to do business. Pre-programmed neural nets will connect security systems and security alerts to identify, detect, and hostile threats, including viruses, worms, trojans, spyware, etc. This insight is crucial for security professionals because it enables businesses to evaluate new cyber-attack methods used by hackers while continuously containing chances of a recent data breach.

The situation helps speed the emergence of relevant growth ventures that offer cyber risk prevention solutions, including preventative insurance and business interruption solutions, as cyber risk matures and new disaster recovery plans are deployed.

5. Security operations centers utilize machine learning to monitor networks 24/7 without human intervention.

Security operations centers (or SOCs) deploy artificial intelligence to monitor networks 24/07 without human intervention. They also detect cyber-attacks before they occur, noticing the pattern of hostile actions to help stop them before they can cause significant reputational damage to an organization.

As human hackers get better at concealing the underlying patterns of their attacks, these advanced machines will uncover new threats quicker than ever. The latter won't be an easy feat for cybersecurity teams required to think through enhanced AI software!

But with enough time and human resources invested into research & development, AI-powered solutions will protect against existing threats and emerging risks faster than experienced before.

So, what do you think? What implications does this technology hold for our society and your business? Are you prepared to defeat new hackers' threats? If you have questions or want to dive into the topic further. Don't hesitate to reach out.