Do you know that cyber threats, especially credential theft, continue to grow, with a 71% year-over-year increase? Yes, the cyber threats are growing more than we can imagine. In this case, cyber threat monitoring is important for robust cybersecurity. However, many businesses are still facing blind spots. This article is especially designed to focus on the limitations of the traditional approaches to help you achieve a complete understanding of the attack surface.
Why Real-time Visibility Matters the Most?
Previously, threat detection was mainly reactive and used to depended on signature-based applications and rule-based solutions to identify the known patterns of criminal activity in network logs and files. Such an approach was efficient when datasets were smaller and risks were well-documents. However, it still lagged behind in coping with the cyber threat, data complexity, and huge volume of data.
The advent of machine learning and deep learning in the sphere of cybersecurity pumped up the detection strategies. This equipped the security teams to find the cyber threat patterns properly and at the pace needed to keep up with the emerging cyber threat techniques.
Presently, the recent evolution of cyber threat monitoring is driven by Artificial Intelligence, mainly Large Language Models. This recent advancement is transforming the way cyber threat detection is carried out, following:
Content to Detection
Although previous ML models could identify the malicious documents, they fell short in explaining the ‘Why’. LLMs, fed with different information such as unstructured threat intelligence reports, can now provide important context behind the move. This can deliver a very informed response instead of a mere binary alert. In turn, the cyber threat mitigation actions could be more targeted and implemented faster than before.
Understanding Complicated Data
LLMs have shown a ray of light in understanding and identifying the malicious purposes with data formats, unlike traditional techniques. This involves log filed, code, JSON, and even malware hashes. As a result of this, the scope of data could be automatically analyzed for identifying the cyber threats.
Challenges of Contemporary Attack Surface
The increasing deployment of cloud-based infrastructure, the continuity of remote work, and the growth of dynamic virtualized assets have blurred the line between traditional networks. This creates a perimeter-less reality, which brings new and complicated risks. The attack surface moves beyond the corporate office, covering cloud misconfigurations, unsafe home networks, and highly transient virtual assets that are challenging to monitor.
This spanned, dynamic attack surface causes potential monitoring issues that old security tools may not address.
Visibility gaps in IaaS/PaaS
Effective monitoring of the cloud environment requires allowing and collecting different log sources like network traffic logs, storage access logs, and audit logs. However, the quality and availability of such data can rely largely on the specific cloud subscription level.
Securing unmanaged devices
The risks move to the individual users and their endpoints with remote and hybrid workforces. Company data can be exploited on personal or company devices used for work purposes, mainly if those are used in Shadow IT or Shadow SaaS practices. Safeguarding these unmanaged personal devices of the employees is a daunting task since companies cannot impose security controls on their systems.
Monitoring transient virtual devices
Contemporary cloud-based environments are increasingly using ephemeral workloads that are transient by nature and may take place for a few minutes. Since these assets are short-term, old security scanning or rule-based monitoring may skip them altogether. This creates a blind spots that the attacker exploit to execute commands or breach data without leaving any evidence.
The growing use of generative AI solutions among third-party vendors also causes specific monitoring issues, mainly in Shadow IT. You can learn more here.
Major Ways to Find Cyber Threats
The very first step to improve cyber threat monitoring is to understand the need for real-time visibility. The next step is to adopt the right methods to get it done. Moving from theory to practice needs the use of proactive and advanced techniques that align with the realities of the sophisticated cyber threat environment. Here are some of the strategies that must be considered:
Adopt an ‘assume breach’ approach
It is time to update the security model and move towards an ‘assume breach’ mindset. As per the traditional approach, anyone involved in the network is already trusted. The major flaw in this strategy is that once an attacker bypasses the security measures through stolen details, malware, or social engineering, they become a trustworthy entity that grants free access to the internal apps and confidential data. However, the modern Zero Trust Security approach works on the standard principle of ‘never trust, always verify’. Such an approach begins with the assumption that a cyber threat has already taken place and that security risks exist both inside and outside the network.
Monitor the dark web
As ‘assume breach’ mindset needs proactive data-gathering beyond the network, an important source for this could be the dark web. This hosts huge illicit marketplaces and forums where confidential corporate data is often exchanged or unfolded following a breach. Contemporary cyber threat monitoring encompasses continuous, automated scanning of the sources, including ransomware blogs, forums, and so on, to find the intelligence relevant to the digital footprint of the organization, like:
- Leaked company or employee credentials
- Exposed sensitive customer information
- Highlights of your brand or executives
- Sale of proprietary company data
By detecting the exposure of the data in real-time, security teams can make informed decisions and take actions like resetting the passwords or informing the vulnerable users.
Address human cyber risks
Although external threats are the primary concern, the human factor remains a key issue when it comes to security cases. Research reveals that human error causes 95% of the breaches. Modern threat monitoring digs deeper to address the issues, using User and Entity Behaviour Analytics to spot internal threats. UEBA is a form of security application that leverages machine learning and behavioural analytics to comprehend the norm within the IT landscape.
Use network traffic analysis
Analysing data flows and metadata set using Network Traffic Analysis can unfold concealed anomalies that conventional firewalls may miss. By monitoring both internal and external traffic, security teams can identify harmful patterns that indicate an active compromise.
Automate endpoint detection and response
Modern endpoint detection and response is not limited to traditional antivirus, but it focuses more on harmful behaviour instead of known file patterns. This behavioural approach helps in spotting the advanced threats. EDR solutions consistently record actions and events on endpoints such as laptops and servers, which in turn improves visibility.
Include AI-driven threat intelligence
Finally, the huge volume of security data generated by the modern organization makes manual analysis problematic. AI is now an important part of exploring the data repositories to find potential threat patterns. Large language models can understand and evaluate a huge variety of formats rather than being limited to simple text. Hence, AI technology can be a potential tool to reduce the dwell time and its related costs.
