Threats to cyberspace are constantly changing. As a result, businesses rely on cutting-edge tools to respond to risks and, even better, prevent them from happening in the first place. The top five cybersecurity trends from last year were previously listed by Gartner. The need for artificial intelligence and machine learning tools to help people remain ahead of the curve is becoming more and more obvious with each passing development.
Even more compelling for this year are these estimates for 2022. To manage cloud environments, remote labour, and ongoing disruptions, businesses will require a versatile, adaptable toolkit powered by AI and ML.
Trend 1: Increased attack surface
Companies are at a turning point as a result of the increase in permanent remote job opportunities. Remote employment has been beneficial for employees and a relief for businesses who weren't sure if their operations would continue after the shift. The drawback is that because these employees need access to company resources wherever they are, businesses have had to move to the cloud, which has exposed more attack surfaces.
Businesses, in Gartner's opinion, ought to think outside the box. And some businesses have without a doubt. By launching sophisticated algorithms that are completely observable, AI can provide continuous monitoring across all settings, managing even the temporary resources of the cloud. In order to give real-time insight into security-related data, for instance, Security Information and Event Management (SIEM) gathers and analyses log data from numerous sources, including network devices, servers, and apps.
Trend 2: Identity System Defense
Similar to trend 1, trend 2 sees the misuse of credentials as one of the most typical ways threat actors access sensitive networks. Companies are putting in place what Gartner refers to as "identity threat detection and response" solutions, and AI and machine learning will enable some of the more potent ones.
For instance, AI-based phishing solutions analyse email content, sender reputation, and email header data to detect and thwart phishing attempts. Businesses can also use anomaly detection. These AI-based detection solutions can employ machine learning algorithms to identify anomalies in network traffic, such as unusual patterns of login attempts or unusual traffic patterns.
When threat actors attempt credential stuffing or use a huge volume of stolen credential information for a brute-force attack, AI can also warn admins. And while it may surprise humans to find how predictable we are, AI can also examine common behaviour patterns to spot unusual conduct, such as login attempts from a different location, which aids in the quicker detection of potential invasions.
Trend 3: Risk in the Digital Supply Chain
By 2025, 45% of firms globally are expected to have been the target of a supply chain assault, according to Gartner. Although supply chains have always been intricate networks, the advent of big data and swift changes in consumer behaviour have pushed margins to precarious levels.
To avoid disruptions, reduce risk, and make speedy adjustments when something does happen, businesses are utilising AI in a variety of ways. With the help of digital twin techniques, hypothetical scenarios may be successfully tested on precise digital supply chain replicas to identify the optimum solutions in almost any situation. It can also do sophisticated fraud detection or use deep learning algorithms to examine network data and find unwanted activity like malware and DDoS attacks. AI-based response systems can also react swiftly to perceived threats to stop an attack from spreading.
Trend 4: Consolidation of suppliers
According to Gartner, manufacturers will keep combining their security services and products into packages on a single platform. While this might highlight some difficulties—introducing a single point of failure, for instance—Gartner thinks it will simplify the cybersecurity sector.
Organizations are becoming more and more interested in collaboration security. Businesses are aware that the digital landscape is no longer confined to a small, on-premises area protected by conventional security technologies. Companies may be able to lessen some of the vulnerabilities present in a complex digital infrastructure by establishing a culture of security throughout the organisation and collaborating with services providing the aforementioned security packages.
Fifth Trend: Cybersecurity mesh
By 2024, firms that implement a cybersecurity mesh should see a significant decrease in the cost of individual security incidents, according to Gartner. There is an obvious benefit that businesses that deploy AI-based security products may experience because these systems can:
- Automate tedious, time-consuming operations, such as incident triage, investigation, and response, to boost the cybersecurity mesh's efficacy and efficiency.
- Utilise machine learning algorithms to analyse data from numerous sources, including network traffic, logs, and threat intelligence feeds, to spot potential security issues in real time and take immediate action.
- Use information from multiple sources, including financial transactions, social media, and news articles, to discover and evaluate any potential threats to the cybersecurity mesh and modify the security measures as necessary.
- Employ machine learning algorithms to find patterns in network traffic that are odd, such as strange login patterns or strange traffic patterns, which can assist in identifying and addressing potential security issues.
Gartner's predictions came true in 2022, but in 2023, we're just beginning to witness dynamic AI answers. Businesses are aware that disruptions and cloud migrations mean that security operations from before 2020 cannot be resumed. Instead, AI will be a critical cybersecurity element that supports each trend and encourages businesses to adopt a completely new cybersecurity strategy.
