With cyber threats becoming increasingly diverse in how they strategically cripple organizations, the cybersecurity landscape is under increasing pressure to bolster its technology and defense methods. Cyberattacks have only become more frequent year-on-year, with the costs to an unprepared business only increasing with them.
Data breaches can harm not only your organization’s wallet but also your reputation. It is therefore imperative that businesses branch out when it comes to data protection, and artificial intelligence (AI) could indeed be the answer cyber operations centers need to detect and prevent threats before they can do any damage.
No Industry Is Safe
Cyberattacks are not limited to one sector. As we have witnessed throughout the past year, no industry is safe. In the healthcare industry alone, 20 million Americans are at risk of “dangerous” healthcare after the CommonSpirit Health cyberattack in October. The major IT breach, caused by a ransomware hack, has affected a system running 140 U.S. hospitals and more than 1,000 care sites, meaning patients across 21 states could still be at risk of receiving faulty care. Cyber actors target hospitals and healthcare providers increasingly to access sensitive patient data, leading to critical consequences for patients, hospitals, and other national health systems globally. These attackers have no remorse; therefore, it is crucial that organizations keep their cybersecurity up to date, especially when lives could potentially be on the line.
Similarly for telcos, governments globally have begun cracking down on employing cybersecurity rules across all mobile and broadband providers. In an effort to protect broadband and mobile networks from potential threats, CSPs need to be more vigilant in their cybersecurity, or else risk fines of up to $100,000 per day should they fail to comply. With governments realizing the importance of investing in modern technology for data protection, businesses across all sectors can benefit from updating their systems, or else risking a healthy pay-out.
It is predicted that, by 2025, cyber-crimes could cost over $10 trillion annually across the world. This estimation is based on growing figures, including factors such as the damage and destruction of data, theft of intellectual and financial property, and also post-attack disruption of business and reputational harm. Organizations must start prioritizing identifying and preventing complex cyberattacks before they occur—something that is impossible if remaining with a legacy system.
Challenges with the Legacy Software
For businesses relying on traditional reactive security monitoring software (such as with legacy SIEM solutions), they have access to basic analysis and aggregation of log data for detecting cyber incidents. Unfortunately, this can be limited, as most solutions only focus on the alert mechanisms to trigger once a previously known attack pattern has transpired. With the dynamically changing threat landscape, a legacy system often does not offer enough organization-wide visibility and scalability to truly prevent attacks should they occur.
Cyber criminals have access to the best software available, meaning even the most advanced security software can be bypassed. Criminals are able to hide their activity in the hundreds of gigabytes of data collected from various log sources, as legacy systems do not have the capacity to learn and differentiate them from common user behavior. When alerts are triggered, these also often are false positives, leading to actual threats slipping through the cracks and going ignored entirely.
Updating legacy systems is therefore imperative. Investing in modern technologies such as cloud-based AI and machine learning (ML)-based threat detection can help IT managers and security operations center (SOC) analysts to be far more proactive in monitoring and preventing any cyber threats, by automatically predicting the behavior of highly complex IT networks and systems.
Being Proactive in Threat Detection
Businesses that are holding on to legacy cybersecurity systems rather than updating and modernizing their technology only grow increasingly ineffective in preventing threats. In relying on being able to resolve issues after the damage has already happened, they are simply allowing otherwise preventable attacks from being perpetrated.
With the right AI system in place, next-generation SIEM solutions can contextualize information to predict cyber threats, rather than just detecting them at the impact stage. Further still, multiple AI models can be used in sequence to optimize the threat detection output to detect early signs of an attack. By integrating with automated data and web scrapers to incorporate the latest contextual threat intelligence for organizations, AI-driven solutions provide near-real-time adjustment ability to reflect real exposure from vulnerabilities, compromised credentials, malicious domain spotting within the context, and risk exposure of any client. Further still, alerts can be prioritized and adjusted based on the potential impact to the organization, putting the most serious alerts at the top of the agenda.
Embracing AI in Threat Detection Is Critical
Predictive threat detection using the potential of AI is critical in ensuring businesses avoid the cost of potentially damaging attacks. Dynamically changing threats have to be combatted with an equally complex and reactive prevention system—something companies must realize quickly to ensure customer data remains safe and protected. AI solutions also help business leaders keep their own peace of mind— less focus or worry about the threat of a destructive cyberattack—and instead more time and money focused on business development.
Ralph Chammah is CEO and Miro Pihkanen is CSO of OwlGaze, a firm that specializes in cybersecurity software and advisory and offers proprietary AI software called Blacklight.