Why Traditional Solutions Fall Short: Need for CyberTransform

The rise of AI is changing the ways organizations work across industries. The increase in availability and necessity of various AI-powered systems serves to make them a greater target for attacks. Traditional security measures, such as firewalls, intrusion detection systems, and encryption, are needed to secure digital infrastructures, but do not provide enough protection against the threats and vulnerabilities of AI systems. 

Attackers may now gain entry without sophisticated programs or breaking through firewalls, and this change has altered the security perimeter slightly. The idea that breaches are unavoidable and that defenders can only react. Organizations must adopt an all-encompassing AI-driven cybersecurity transformation for cyber risk management in order to adequately protect AI Models and applications from these ever-changing threats. 

How AI Has Changed the Threat Landscape

AI systems make judgments, process enormous volumes of data, and frequently function independently. The vulnerabilities are a major worry as AI gets more integrated into vital applications like financial trading, autonomous driving, and healthcare diagnostics. Even conventional predictive AI, which has been used for many years in various industries, is vulnerable and might have a significant negative effect on businesses if companies fail to adapt to cybertransform. The incidents of AI-specific vulnerabilities will emerge at varying points in the life cycle of AI. These include:

• Data Poisoning: AI Models are trained on enormously extensive datasets, and the efficiency and performance of such models may be negatively affected if the adversary is able to access or corrupt the data.
• Model Inversion: The invader can threaten privacy by utilizing outputs of the AI systems to reverse engineer the secret data that has been utilized in training the system used. 
• Adversarial Attacks: Adversarial instances are falsified inputs generated so as to delude the AI that they are giving appropriate outputs, while in reality, they inherently induce erroneous forecasts. Speech and image recognizing AI systems are the most susceptible to adversarial instances.
• Model Extraction: By frequently querying a machine learning model to deduce its underlying methods and parameters, cybercriminals can extract and steal intellectual property.

Traditional cybersecurity advisory is unable to offer AI systems complete protection in light of these particular dangers. 

Besides AI-driven cybersecurity attacks, there are also evolving trends in threats. These include:

• State-sponsored Attacks and Advanced Persistent Threats (APTs): These attacks, which are premeditated, frequently employ sophisticated methods of penetrating networks, stealing confidential information, and disrupting daily operations. The identification and reduction of such threats are frequently beyond the ambit of conventional cyber risk management methods, including firewalls and anti-virus software.
• Multi-Vector assaults: Phishing emails, malicious websites, and compromised IoT devices are just a few examples of the multi-vector assaults that cybercriminals are increasingly employing to target several access points at once. Traditional cybersecurity advisory that concentrates on a single point of defense may be overpowered by these attacks.
• Enhanced Ransomware Sophistication: Ransomware attacks now go far beyond primitive encryption and introduce data exfiltration and double extortion techniques.

Limitations of Traditional Cybersecurity Approaches

The standard security stack was created with the underlying assumption that all actions and modifications are authorized unless they are known to be malicious in order to identify and stop threats. This method enables attackers to create novel strategies and malicious programs that are unidentifiable and missing from any known risk. Below are some of the areas where traditional cybersecurity advisory falls short.

• Endpoint Protection 

Signatures and behavioral analysis are used by endpoint protection systems, such as antivirus (AV) and endpoint detection and response (EDR), to identify harmful activity. However, modern attackers have learned to use advanced techniques, including polymorphic malware, attacks living off the land (LotL), and fileless malware, to defeat detection. These threats are difficult to identify in real time, even with next-generation cyber risk management solutions.  

• SIEM: Missed Signals and Data Overload

SIEM technologies produce an excessive number of security events and warnings and gather enormous volumes of records. They still overwhelm security professionals, even after thorough tuning. Without an AI-driven cybersecurity transformation, it is simple for little indications of an attack to be overlooked due to the constant overload of alert fatigue. 

• Vulnerability Control: Patching Is Insufficient

Organizations can use vulnerability scanners to find and fix “known” vulnerabilities. However, attackers take advantage of known and unpatched zero-day vulnerabilities. Many companies’ patching procedures are delayed, leaving systems vulnerable for weeks or months at a time. 

• A Reactive Approach to Threat Detection and Response

Indicators of compromise (IOCs) and post-event investigations are the foundation of threat detection and response solutions like managed detection and response (MDR) and extended detection and response (XDR). An attacker may have already obtained permanent access to add, edit, or remove anything by the time a threat is discovered. Because of this reactive strategy, organizations are always catching up.

Bottom Line

With the commencement of cybertransform, which has now reached a critical stage with an adequate number of enterprises now undergoing the process, traditional forms of attack are no longer either sufficient or effective. Organizations must recreate their cyber risk management strategy to combat the new hazard, employing pre-emptive security measures, adopting precepts of the Zero Trust approach, and investing in genuine instruments of real-time integrity examination (monitoring).

So that security professionals can react quickly and accurately. It is time to adopt an AI-driven cybersecurity transformation that streamlines operations, dismantles silos, and provides a wider perspective of the attack surface. 

Guest Author