SOC (Security Operation Center) Part — 3

Summary of SOC Part — 2

• In a nutshell, multi-cloud or hybrid configurations are operated by a robust security operations model for threat monitoring, threat response, and incident management for security. Combined cloud-native security tools, SIEM systems, and threat intelligence platforms help monitor, detect, and mitigate risks in real-time. Some of the techniques include granular IAM, data at rest and in motion encryption, continuous auditing, among many more that improve the security posture.
• Data privacy and protection are equally relevant, particularly in the case of SOC. It limits exposure through a restricted number of entries, encrypted access to sensitive data, and proper private protocols of incident response processes. Privacy training is conducted at regular intervals; SOC processes follow Privacy-by-design principles, thereby ensuring maximum compliance with GDPR and HIPAA regulations.
• Monitor the deep and dark web as a complement to many of the traditional controls — provide early warnings of breaches, compromised credentials or emerging threats. This will be managed through a combination of automated and manual monitoring techniques, plus.
• Cybersecurity is now rapidly accelerating at speed that has never been experienced. Relatively new and emerging threats and vulnerabilities increasingly come up in the technologies that range from blockchain security to risks associated with synthetic media. Advanced strategies and new tools are the only things that can befall organizations as they seek to stay ahead of such changes. It describes the difficulties that need to be addressed in order to monitor and respond to the blockchain network, DeFi platform supply chain attack, insider threat, and deepfake media security events, by integrating the application into SOC operations using practical case studies.

Cryptocurrency and Blockchain Security Monitoring

Blockchains and Cryptocurrencies have brought forward new security problems. This is how the organizations will overcome the problem:

Techniques for Blockchain Network Monitoring and Fraud Detection

This monitoring in blockchain networks is through transaction trace, anomaly detection, and fraud identification. Techniques include smart contract auditing, real-time transactions, and AI-based anomaly detection with measures to prevent fraud. Techniques critical against double spending or Sybil attacks are checking nodes’ health, changes in consensus, and unusual movement of tokens.

Integrate Cryptocurrency Threat Intelligence with SOC Operations

Cryptocurrency threat intelligence is thus integrated into the operations of the SOC to identify and mitigate blockchain-based security incidents. These include tools used for gathering intelligence such as suspicious wallet addresses, transaction patterns, and cryptocurrency exchange threats from Chainalysis and CipherTrace. This integration gives an all-round view of blockchain threats in conjunction with traditional SIEM systems.

Case Studies on Blockchain-Based Security Incidents

Probably, one of the most prominent cases is the KuCoin exchange hack case in 2020, where it got hacked and then over $280 million worth of cryptocurrencies were stolen. With another analysis, the investigator can trace back to the blockchain transactions that the hacker followed in getting a part of the stolen cash. Real-time monitoring of the blockchain on such an incident has now become seriously crucial.

Advanced Threat Detection in DeFi Platform

Such a system has been highly reliant on the DeFi systems, making such a system very critical to attackers. The detection mechanisms include automatic detection of vulnerabilities in smart contracts, anomaly monitoring of a liquidity pool and feeds dedicated to threat intelligence related to DeFi can detect possible risks ahead of large-scale losses like that in the 2021 Poly Network attack in which a threat actor successfully made off with $600 million threatening a smart contract.

Sophisticated Supply Chain Attack Monitoring Advanced supply chain attacks target the organizations through vulnerabilities of third-party vendors. Here’s how to avoid them: Discovery and Mitigation of Supply Chain Threats

Scan and monitor third-party software; recognize vulnerability within the third-party dependencies; track the integrity of the software using the linked digital signatures and hashes form an efficient supply chain threat monitoring. Real-time risk mitigation takes place through alerts from third-party software that are compromised in terms of change in its integrity.

Third-Party Risk in SOC Operations

This incorporates third-party risk into the operations of the SOC.

Thus, it will be able to incorporate third-party risk management with its operations to become a successful SOC. This would include constant assessment of the risks based on the vendors involved, monitoring for anomalies in the systems of third parties, and integration of the third-party risk data into SIEM and threat intelligence systems.

Some high-profile case studies about supply chain attacks

A good case in point would be the 2020 SolarWinds attack, which had seen attackers penetrate into the SolarWinds Orion software and then proceed to access thousands of organizations, including government agencies. It makes a very good case for having advanced monitoring and real-time response capabilities for third-party software.

Advanced monitoring and real-time response techniques used in software dependencies

To that, SOCs need to track vulnerabilities in their dependencies using tools like GitHub’s Dependabot and SBOM analysis. Automated mechanisms to patch or respond to mitigation of risks arising from vulnerable dependencies-this is what happened during the 2021 attack on Codecov.

Cyber Threat Intelligence Fusion Centers

Threat intelligence fusion centers enhance the capability of an SOC, through them, aggregated data from more than one source can enable real-time identification of threats and response to the same.

Building and Operating a Threat Intelligence Fusion Center Building of a threat intelligence fusion center requires integrative data streams coming from various sources of which include OSINT to dark web intelligence and vendor-specific threat feeds. Such centers are critical in the efforts towards the improvement of detection of threats and accommodation of various incidents.

Advanced Techniques for Fusing Multiple Sources of Intelligence

This data from the threat feeds, security logs, the dark web, and social media, where all these fuses into a more extensive view in relation to possible threats, is brought together. There is the basis of correlation-based data by using machine learning algorithms; there comes a pattern, along with some predictive future attacks in response time improvement.

Collaborative Threat Intelligence Sharing

Organizations still share intelligence related to emerging threats through systems such as CTA and ISACs. Documentation and sharing of threating data will contribute much towards the reduction of time taken for detecting and responding to threats across the globe.

Real-time Threat Fusion and Its Impact on SOC Operations

Real-time threat fusion allows SOCs to achieve a real-time view of events for faster response as well as integrates under one roof the view of threats in different environments. The false positives are also reduced, and precision in the detection of threats is improved.

Advanced Insider Threat Detection

The number one security challenge is insider threats, either malicious or accidental. Techniques bringing behavioral analytics along with human resources can act as one big mitigator of such risks.

Any pattern of probable insider threat involvement requires performing any form of behavioral analysis of the users using monitoring tools. Psychometric analysis refers to the study of emotional and cognitive behavior of people to identify the vulnerable population at risk of turning against the organization as an insider threat.

As illustrated in Figure, HR data can be integrated with SOC monitoring tools.

Human Resource Data Consolidation: This would include employee position changes and performance ratings, in addition to notice of employee departure related to SOC tool. It could provide an analyst with even more context from behavioral anomalies that could have potentially been insider threats.

Privileged Access and Data Exfiltration Advanced Techniques

A comprehensive privileged access monitoring toolkit would at least include User and Entity Behavior Analytics and Data Loss Prevention systems, both of which would monitor abnormal behavior.

Other sophisticated tools include automation of alerts on suspicious access to files, encryption of data transmissions, and exfiltration of sensitive information.

Complicated Insider Threat Incidents Case Studies

A classic case is Edward Snowden, where sensitive NSA data was exfiltrated because of weak access control and no monitoring of privileged accounts. This is where continuous monitoring and anomaly detection play a significant role in high-security environments.

Deepfake and Synthetic Media Detection

Deepfakes and synthetic media have brought a form of warfare altogether into the realm of cybersecurity, highly prevalent in disinformation campaigns and social engineering attacks.

It is through AI-based techniques which have embedded facial recognition algorithms, audio analysis, and forensic media file analysis which are said to possess the actual effectiveness of the detection of deepfakes. The process of detection utilizes the software tools; these include Microsoft’s Video Authenticator and Sensity AI, which detect altered media in real time.

The workflows of the SOC integrate deepfake detection tools that complement the prevention of disinformation campaigns and social engineering attacks. Alerts can be sent for any manipulated media found in corporate communication channels or the public open platform.

Incident Documentation Involving Synthetic Media

Deep logging and analyses are essential for organizations to make sure they protect themselves against such incidents in the future. It was just recently that a deepfake audio impersonation attack which withdrew $35 million from the bank executive’s account. Proper logging of such an incident would build much stronger defenses against such incidents. Advanced AI and Machine Learning Models for Media Analysis

Advanced AI models are currently trained to detect even the slightest manipulations in video, audio, and images, putting alarms on its owners. Certain methods, such as GAN, can create and detect deepfakes and this too is one of the main lines of defense.

Conclusion

Blockchain security to the detection of deepfakes will haunt the changing cybersecurity panorama in organizations. Advanced threat intelligence, behavioral analytics, and monitoring tools should characterize the SOC operations with a robust security posture overall. That way, using such emerging techniques and tools shall grant organizations an upper hand over their adversaries.

Written by- Karan Kachadiya