Cyber-attacks are growing in number, frequency and sophistication that often cannot be addressed by traditional security measures. According to an IBM study, 68% of responders to cybersecurity incidents reported that they manage multiple incidents simultaneously. Moreover, threat actors are leveraging AI to create deepfakes impersonating high-ranking executives, draft highly personalized phishing emails and develop undetectable malware. Thus, the integration of AI into cybersecurity has become not just a luxury, but a necessity. It enables security teams to multiply their efforts with the exceptional capabilities of AI in processing large amounts of datasets, identifying patterns and responding to incidents quickly, effectively and accurately. Additionally, AI can support small and medium enterprises with budget constraints in managing skill gaps and accelerating their cyber resilience efforts. This article explores how organizations can strengthen their cyber resilience by integrating AI into their risk management, incident management, and cyber resilience testing.
The use of AI in risk management
In terms of risk management, an organization’s first step should be to identify, classify and document all assets, including roles and responsibilities, information assets and ICT systems. Due to cloud services, Internet of Things (IoT) and shift to remote work, assets tend to be highly distributed making it complicated to identify and manage. Fortunately, AI-powered asset management systems such as IBM Maximo Application Suite, SAP Intelligent Asset Management and Google Cloud Asset Inventory, can help with the discovery, classification and documentation of all devices, applications and users. With up-to-date asset inventory, organizations can effectively perform risk assessment against any threats and vulnerabilities.
This leads us to the next step: risk assessment. To mitigate risks effectively, organizations must identify all risks associated with business operations, information assets, and ICT assets. However, conducting a manual risk assessment can be costly, time-consuming and challenging depending on the size of the company, complexity of services and number of risk factors. An AI-based risk assessment tools including IBM OpenPages, SAS Risk Management, and Azure OpenAI Service can not only address the above challenges, but also provide solutions to manage the identified risks.
Based on the results from the risk assessment, your organization may develop policies to allocate roles and responsibilities, manage all identified risks, and ensure regulatory compliance. It is crucial that these policies are implemented by all employees, partners, and third-party service providers. To simplify policy enforcement, monitoring and management, organizations making use of AI-powered solutions such as IBM OpenPages Policy Management and Microsoft Azure Policy. For instance, these solutions can automatically enforce policies related to identity management, access control and device authentication.
Moreover, AI can be used for up-to-date, adaptive and personalized security awareness programs and training for all employees including senior management staff. According to IBM Cost of a Data Breach Report, up to 95% of cybersecurity breaches are caused by human error, which can be significantly reduced through proper training. Organizations can use platforms such as SoSafe Cybersecurity AwarenessandCybSafe Personalized security awareness training to reduce incidents caused by human error.
The use of AI in incident management
In the previous section, we covered risk identification, risk management, policy enforcement and employee training to protect your organization from cyber threats. To ensure continuity of business operations, organizations must be capable of detecting, responding and recovering from incidents. The sophisticated cyber threats are almost impossible to detect with traditional methods and human analysis. According to IBM insights, organizations that take 230 calendar days to detect, respond to, and recover from cyber incidents can reduce that time by up to 99 days using AI and automation tools.
To begin with, AI-based tools such as IBM Databand Data anomaly detection and Microsoft AI Anomaly Detector are widely used in detecting anomalies by processing log data, analyzing network traffic and user behavior. For instance, it can establish baselines for normal user behavior and flag deviations that might indicate a compromised account. Furthermore, AI is much faster and more efficient in detecting spam emails by analyzing the content of emails. AI-powered tools such Cisco Umbrella and Microsoft Defender for Endpoint can be used to block malicious domains and catch any malicious network traffic.
When a threat is detected, AI can initiate automated responses to stop the threat and minimize the impact. AI-based incidence response solutions including Microsoft Sentinel, IBM QRadar and Palo Alto Networks Cortex XSOAR can lead to effective, quick and reliable response as it can learn from past incidents and improve the accuracy of its responses. The main advantage of AI is that it continuously learns from new data and evolves to effectively counteract new cyber threats. In addition to that, the above solutions can assist security teams in automatically allocating responsibilities and assigning incident response duties based on the nature, scale and severity of an incident.
Finally, some organizations have an incident reporting obligation under EU and national laws. In that case, organizations can deploy AI powered solutions including Microsoft XDR to streamline reporting and auditing requirements.
The use of AI in cyber resilience testing
To ensure that your organization is resilient against cyber threats, it is critical to conduct regular cyber resilience testing in order to identify any weaknesses, vulnerabilities and gaps. Manual testing is generally resource-intensive, time-consuming and demands highly skilled security professionals. Without automated or AI-powered tools, it is challenging to test every component of the system or cover every scenario. Whereas AI-driven tools such as MITRE Caldera, SafeBreach, Microsoft Sentinel and IBM QRadar can simulate various tactics and techniques used by real-world attackers and scan large networks and systems in a fraction of time. It can be scheduled to run frequently, allowing continuous monitoring and assessment of security postures.
Final Remarks
As cyber threats continue to evolve in complexity, integrating AI into cybersecurity is crucial for organizations seeking to enhance their cyber resilience. AI-driven tools have tremendous potential and capabilities to improve risk management, detect and respond to incidents swiftly, and automate security testing to identify vulnerabilities. These technologies empower businesses to stay ahead of cybercriminals while reducing human error and ensuring compliance with regulations. Eris Law Advokatbyrå AB specializes in guiding organizations through the legal complexities of cybersecurity and helping you implement AI solutions that meet regulatory standards. Our next articles will keep you updated with latest laws and regulations on cybersecurity, ensuring your organization is prepared for the future.