AI will become a key component of cyber defense strategies in 2023, enabling businesses to move to an entirely new approach to cybersecurity.
Cybersecurity threats are evolving rapidly. This is why businesses are turning to innovative tools to respond to threats and even prevent them from the start. The following top seven cybersecurity trends of the last year can be outlined. Each time, it becomes clear that humans will need the support of artificial intelligence and machine learning tools to stay ahead of the curve.
These predictions for 2022 are even stronger this year. Enterprises need flexible, dynamic, AI/ML-driven tools to manage cloud environments, remote work, and ongoing disruption.
Trend 1: Expanding Attack Surface
Enterprises are at a crossroads with the rise of permanent remote work positions. Remotes have been a plus for workers and a sigh of relief for businesses that were unsure their operations would hold up to this shift. is what you need to do. This will force companies to shift their operations to the cloud– and it exposed a broader attack surface.
Companies should look beyond traditional approaches. And some companies certainly have. AI kicks in with a sophisticated program designed for full observability to handle even the ephemeral resources of the cloud and enable continuous monitoring across all environments. For example, Security Information and Event Management (SIEM) aggregates and analyzes log data from various sources such as network devices, servers, and applications for real-time visibility into security-related data.
Trend 2: Identity System Defense
Related to Trend 1, Trend 2 looks at credential abuse as one of the most common ways threat actors gain access to sensitive networks. Companies are setting up what is called “identity threat detection and response” tools, and AI and machine learning power some of the most powerful tools.
For example, AI-based phishing tools use machine learning algorithms to detect and block phishing attempts by analyzing email content, sender reputation, and email header information. Additionally, enterprises can leverage anomaly detection. These AI-based detection tools use machine learning algorithms to detect anomalies in network traffic, such as unusual patterns of login attempts and unusual traffic patterns.
AI can also alert administrators when threat actors attempt credential stuffing or use large amounts of stolen credentials for brute force attacks. While humans may be disappointed to learn how predictable humans can be, AI can also analyze typical behavioral patterns to detect anomalous behavior, such as login attempts from new locations. can. This allows potential intrusions to be detected more quickly.
Trend 3: Digital Supply Chain Risk
45% of organizations worldwide will experience some type of attack on their supply chains by 2025. Supply chains have always been complex networks, but the addition of big data and rapid changes in customer behavior have made margins very thin.
Businesses are using AI in every possible way to prevent disruption, reduce risk, and quickly pivot when something happens. The tool can successfully run virtual scenarios against an exact digital replica of your supply chain and find the best solution in almost any scenario. It can also participate in advanced fraud detection, leverage deep learning algorithms to analyze network traffic, and detect malicious activity such as malware and DDoS attacks. Additionally, AI-based response systems can quickly respond to perceived threats to prevent the spread of attacks.
Vendors will continue to integrate security products and services into packages on a single platform. While this can make certain challenges more prominent, such as introducing single points of failure, but it reduces complexity in the cybersecurity industry.
Collaboration security is also growing in popularity among organizations. Enterprises recognize that their digital landscape is no longer a narrow, on-premises confine handled by traditional security capabilities. By fostering a culture of security across the enterprise and partnering with a service that offers the aforementioned security packages, enterprises can hope to mitigate some of the inherent weaknesses of complex digital infrastructures.
By 2024, cybersecurity mesh will significantly reduce the economic impact of individual security incidents. This is a clear potential benefit for companies adopting AI-based security tools, as AI-based systems can:
- By automating repetitive and time-consuming tasks such as incident triage, investigation and response, you can improve the efficiency and effectiveness of your cybersecurity mesh.
- It uses machine learning algorithms to analyze data from various sources such as network traffic, logs, and threat intelligence feeds to identify and respond to potential security threats in real-time.
- It uses data from various sources such as financial transactions, social media, and news articles to identify and assess potential risks to your cybersecurity mesh and adapt security measures accordingly.
- Leverages machine learning algorithms to detect network traffic anomalies, such as unusual patterns of login attempts and unusual traffic patterns. This helps identify and respond to potential security threats.
- Deploy machine learning algorithms to detect and respond to security incidents and automatically implement countermeasures to prevent similar incidents from occurring.
- It integrates with other security tools such as firewalls, intrusion detection systems, and SIEMs to provide a comprehensive and coordinated security solution.
Each of these capabilities enables the establishment of a cybersecurity mesh.
Trend 6: Decentralized Decision-Making
The digital environment is too complex for one CISO to make all the decisions. The role of the CISO will expand, allowing carefully positioned leaders to enable decentralized decision-making while still allowing the CISO to set policy.
AI-supported decision-making is key to this evolved CISO function role. Automation and advanced observability give leaders a real-time view of the situation and receive actionable steps to mitigate or pivot based on the latest data. In some cases, automation reduces the need for human decision-making in the area of interest, freeing up humans to focus on more complex troubleshooting and response.
Traditional security responses are no longer effective in today’s evolving security environment. Human error is still the cause of most security incidents, and organizations must move to a far more progressive and holistic approach than traditional awareness campaigns.
This means using AI for more than just prediction. AI can analyze user behavior for anomaly detection, dynamically adjust authentication requirements based on real-time risk assessment, and learn from each incident to ensure scale and flexibility in threat detection. Moreover, as attackers themselves are using AI to break traditional security patterns, deploying AI to counter these attacks is the only way forward for them.
AI will become even more essential for cybersecurity in 2023
In 2023, AI is poised to play an increasingly crucial role in cybersecurity. As businesses grapple with disruption and the shift to cloud-based operations, they recognize that relying on pre-2020 security measures is no longer sufficient. AI will be integral to addressing emerging threats and revolutionizing cybersecurity practices, paving the way for a more comprehensive and effective approach.
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