Category : Cybersecurity


Designing a Comprehensive Cybersecurity Framework 

The swift expansion of the digital economy has resulted in a notable increase in digital crimes. Industry reports indicate that companies face annual costs in the trillions to address cyberattack damages. Adopt the ISO27001 framework to design a Cybersecurity framework program for your organization.

By the end of 2023, Cybercrime Magazine predicts a colossal $8 trillion in yearly cybercrime damage costs. This surge in the frequency and sophistication of attacks places cybersecurity at the forefront of modern business concerns. It highlights the acute need for organizations to manage risks by adopting a robust cybersecurity framework.

IT Landscapes are Growing and Becoming More Complex

As business leaders accelerate digital adoption, the IT landscape is expanding, accompanied by increasing security challenges. Additionally, the adoption of hybrid work models adds complexity to IT networks.  

The increased use of advanced AI and ML technologies by attackers to plan sophisticated cyber-attacks, the surge in machine and IoT identities, the increase in the interconnectivity of Information Technology (IT) and Operational Technology, such as shop floor equipment, etc., and the rise in cloud adoption expands the growing risk landscape. 

Hence, adopting a structured approach to creating a cybersecurity framework is essential.

The Imperative for a Systematic Approach 

Basic security measures such as firewalls and antivirus software cover some aspects of security and provide some level of defense. However, these measures do not cover other security aspects such as application security, end-point security, etc. Therefore, they are the ad-hoc approach. Hence, we require a systematic approach to cybersecurity.

Creating The Cybersecurity Framework – Compliance-led approach  

A cybersecurity strategy that prioritizes a compliance-led approach helps align an organization’s security practices with established industry standards. This ensures that security controls are in place to nullify all potential attack options. Thus, organizations adopting a compliance-led approach to steer their cybersecurity strategy can ensure regulatory compliance, holistic security, reduced risk, robust data protection, and data governance.

To develop a compliance-led approach, organizations must comply with multiple standards beyond just one to cover all security aspects. For instance, ISO 27001, NIST (National Institute of Standards and Technology), or Mitre Attack lay a foundation, industry-specific standards like Health Insurance Portability and Accountability (HIPAA) for healthcare and PCI/DSS (Payment Card Industry Data Security Standard) for credit card industries, demonstrate the need for a tailored approach.

While these standards prescribe a general approach and controls, adapting the standards to align with the organizational security needs is essential to creating a cybersecurity framework.

Key Elements of a Cybersecurity Framework 

  • Discovery and Cataloging: Identifying assets, processes, systems, and stakeholders.  
  • Data Classification: Assessing the value of each information asset.  
  • Data Value Quantification: Evaluating and assigning values to the likelihood and impact of identified risks.  
  • Vulnerability Identification: Identify Vulnerabilities in the organization’s IT and OT systems.

Discovery and Cataloging 


Data discovery involves identifying, collecting, and analyzing data. The steps involved are:

  1. Identify the data sources.
  2. Understand system usage and its accessibility, determining who accesses it (such as user groups and applications) and their level of access.
  3. Categorize the data based on the types of value it can create, such as:

    • Financial value: Data that can generate revenue or reduce costs.
    • Operational value: Data that can improve efficiency and productivity.
    • Strategic value: Data used to develop new products and services, identify new markets, and make better decisions about resource allocation.
  4. Investigate the interdependency of data, its origins, integrations, and usage.

Data cataloging is crucial for enhancing data comprehension, improving data quality, and implementing governance policies such as retention, access control, and more. It involves:

  1. Creating a data map.
  2. Analyzing and constructing a data dictionary that includes:
    • Data element groups (e.g., Employee, Payment, Receivables, Procurement, Operational).
    • Information groups (e.g., Employee Salary-related, Vendor financial information, customer details).
    • Data sources (System names where data resides).
    • Dependent systems (Systems utilizing this data element).
    • Brief descriptions of data elements.
    • Compliance mapping (identifying applicable compliance requirements).
    • Storage types (e.g., data field, image, PDF).
    • Storage locations (e.g., Vendor app cloud, MN8 database, or cloud).
    • Data owners (e.g., HR, Finance, Operations).
    • Retention duration (length of data history maintained).

Data Classification  

Data classification, often under-emphasized in cybersecurity, determines an organization’s data value. It guides the direction of cybersecurity investments for the highest return on investment. Allocating substantial funds to protect critical data (‘crown jewels’) is more effective than securing all data, especially when the loss of some data would have minimal monetary impact on the organization. 

It’s crucial to differentiate between high-value and less critical data, organizing and labeling them effectively. For this purpose, leveraging artificial intelligence and other advanced technologies can be highly beneficial. 

Data that is high-risk or sensitive needs extra care. Ask yourself the following questions to help lower the risk of data breach or loss:

  • Do I need to make a copy of restricted data?   
  • Do I need to share restricted data with someone else?   
  • How long must I keep a copy of restricted data?

Data Value Quantification 

Organizations must clearly understand and quantify the value of their data. This crucial step guides allocating effort and financial resources toward data protection. The process includes:

  • Estimating the nature of potential attacks, such as data theft or data unavailability. 
  • Assessing the annual likelihood of such incidents. 
  • Calculating the annual monetary loss – the Risk Amount – by combining the data’s value and the estimated incident frequency. 

With the financial quantification of risks, organizations can prioritize and focus spending on protecting their most valuable data.

Vulnerability Identification

Identifying systems, software, and network vulnerabilities is crucial to mitigate risks before attackers can exploit them. Various methods are available for this purpose: 

  • Manual Techniques: These require prior knowledge of the existing systems, software, networks, and their vulnerabilities. 
  • Automated Tools: A range of automated tools can scan systems, software, and networks for known vulnerabilities. 
  • OSINT (Open Source Intelligence): This method identifies potential vulnerabilities by gathering information from publicly available sources, such as websites, forums, and social media. It also includes threat intelligence from cybersecurity firms, government agencies, and private sector organizations.

Creating a Security Framework Leveraging Compliance Standards

The diagram below represents the 4 security control sections and 11 new controls out of 93 controls prescribed by the recently updated version of ISO 27001 (the previous version of ISO 27001 included 14 security controls comprising 114 controls). Organizations adapt and adopt these controls depending on their maturity level to formulate a Cybersecurity Framework (CSF). The CSF outlines policies and procedures and encompasses five core functions: Identify, Protect, Detect, Respond, and Recover.  In line with the above, organizations actively implement projects that align with these standards, thereby managing controls more effectively.

Adopt ISO27001 framework to design a Cybersecurity framework for your organization

The Benefits of a Compliance-Led Cybersecurity Approach  

A compliance-led cybersecurity approach offers numerous advantages for organizations, particularly those operating in highly regulated sectors. Here are some key benefits:

  • Regulatory Compliance: Achieving and maintaining regulatory compliance shields organizations from legal repercussions and ensures adherence to industry-specific standards.
  • Data Protection: Robust security measures safeguard critical data, reducing the risk of breaches and unauthorized access.
  • Reduced Risk: Strong security protocols protect vital data, minimizing the chances of breaches and unauthorized entry.
  • Improved Data Governance: Clear data governance policies enhance data quality and management in addition to security.
  • Holistic Security: A comprehensive and structured approach leaves all vulnerabilities addressed. 
  • Business Continuity: Incident response and recovery plans ensure uninterrupted operations in the face of cyber incidents.

As we move forward in the digital age, it becomes increasingly clear that cybersecurity is not just a defensive measure but a strategic imperative. The insights discussed in the blog demonstrate the necessity of evolving with technological advancements and emerging threats. A comprehensive cybersecurity framework is the cornerstone of safeguarding an organization’s digital assets and fostering a culture of innovation and resilience.  

With data as valuable as currency, such a framework becomes a key differentiator in the marketplace. It enables organizations to pursue new opportunities while securely and confidently managing their digital footprint. Adopting this proactive approach to cybersecurity positions organizations as defenders and pioneers in shaping a secure and sustainable digital future.

The Robosoft Advantage 

At Robosoft Technologies, we recognize the importance of both compliance-led and risk-based cybersecurity strategies. Our expertise in Cybersecurity, covering areas such as Application Security, Cloud Security, Endpoint Detection and Response (EDR), and Data Security, positions us as your ideal partner to strengthen your cybersecurity posture. Reach out to us today to fortify your organization against cybersecurity threats.

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Rapid Adoption of AI in Cybersecurity

Organizations have been scampering to protect data, identities, and other digital assets from Cyber Attacks. AI has further strengthened the hand of Cybercriminals, enabling them to unleash a new generation of threats. Cybercriminals are using ML to improve algorithms for guessing passwords using AI/ML ability to quickly analyze large password data sets. On the other hand, Cybercriminals can use Deepfakes to spread political or societal misinformation or even craft non-consensual porn of unwilling people. AI algorithms can analyze communications and craft convincing phishing emails to deceive recipients into disclosing sensitive information or initiating fraudulent transactions. AI can automate and enhance ransomware to target valuable assets. AI can be leveraged to evade security measures and exploit vulnerabilities in the business system. 

The future evens out the advantages by pitting machine against machine. AI in cybersecurity is being used to detect what is normal business functioning and spot any deviations in real-time to enable quick response and improve the overall accuracy and efficiency of defense against cyber attacks. Going beyond the dated conventional approach of detecting threats based on signatures, AI in cybersecurity solutions identify behavioral patterns such as using specific ports at a particular time, a sudden surge in data download, and specific patterns or anomalies to detect an incident and take action before substantial damage is caused. 

Thus, AI in cybersecurity offers the benefits of faster threat detection, proactive response, and greater scalability by evaluating network traffic patterns, analyzing large amounts of data to identify potential threats that may not be immediately apparent to humans, automating routine monitoring and reporting tasks, and reducing cybersecurity costs. AI can manage large and complex networks with ease. By incorporating AI in cybersecurity strategies, businesses can augment their security measures and work with a higher degree of confidence.

How AI Can Tackle Threats of Cyber Attacks

use of AI in cybersecurity to tackle threats of cyber attacks

AI in cybersecurity has emerged as a valuable tool to combat the ever-evolving sophistication of complex cyber threats. AI is being smartly deployed to counter cyber attacks. AI is being used to automate the deployment of deception technology to misdirect attackers or lure them into a trap. Some of the ways in which AI in cybersecurity helps stay a step ahead are:

#1. Threat Detection and Intrusion Prevention

AI and deep learning algorithms quickly analyze enormous amounts of data and patterns to uncover potential threats and detect anomalies in network traffic or user behavior. Using Artificial Intelligence in cybersecurity, organizations can transition from the traditional methods that rely on administrators to specify good or bad application traffic. 

Unsupervised deep learning algorithms can develop a positive security model of your application in real-time to promptly identify and respond to cyber threats. This proactive approach allows for prompt detection and response to cyber threats, making AI in cybersecurity an invaluable tool for safeguarding against evolving risks. 

#2. Malware Detection

Detection and Prevention based on signatures are reactionary and cannot contain the initial damage that malware can cause. With AI in cybersecurity, the approach is to detect and classify malware by analyzing behavioral patterns, code patterns, and file characteristics. Supervised deep learning can train multiple models with large data sets and apply the trained models to detect malware behavior and identify new and evolving malware variants. 

Using deep learning algorithms to model malware activities can sometimes result in false positives and false negative misclassifications. It may misclassify malware activity as benign—a false negative, or benign activity as malware—a false positive. Combining the modeling of both bad and good behavior can improve the success rate. This switch from trapping (based on signatures) to hunting (based on modeling behavior) tried to detect sudden excessive use of computing resources by monitoring abnormal requests, connection requests from non-validated sources, the abnormally high quantum of data getting transferred externally, typical requests coming in at atypical times, and so on.  

ML is ideal for anti-malware protection since it can draw on data from previously detected malware to detect new variants, including identifying covert channels and file-in-file activities. While hunting requires substantial computing resources and continuous and ongoing improvement, it is the best approach for proactive defense. 

#3. User Behavior Analysis

Threats extend beyond external sources, encompassing internal risks as well. However, Artificial Intelligence in cybersecurity can address such challenges effectively. AI models can be set up to determine standard user patterns and quickly identify any deviations. Computing enables round-the-clock monitoring of any such suspicious behavior and mitigates insider threats. 

#4. Identify Vulnerabilities

Every organization digitally stores sensitive data, such as financial, personal, customer, and similar information, which is constantly under cyber threat. Today, various vulnerability scanners come equipped with comprehensive automated tests. These tests are designed to efficiently scan and analyze systems, applications, and networks to pinpoint potential vulnerabilities attackers can exploit. AI models rank these vulnerabilities in the order of risk they pose to enable prioritized mitigation. 

#5. Phishing and Fraud Detection 

AI models effectively analyze email content, URLs, and other characteristics to recognize and flag abnormal patterns for removal. Also, AI models analyze vast piles of historical data to recognize patterns and undergo continuous improvement by analyzing real-time data to stay a step ahead of phishing attacks, which are getting more sophisticated by the day. 

#6. Identity and Access Management 

Simple algorithms have helped gauge password strength and encourage users to have a stronger password. Multifactor authentication is already the norm in most places. Today, algorithms can detect compromised credentials by analyzing various factors, such as user behavior, the location of access requests, and the characteristics of the devices used to access the account. By considering all these factors, the algorithms can determine the authenticity of the access requests and prompt additional information if any suspicious activity is detected. 

AI in cybersecurity strengthens the security layer by using tools like facial recognition and fingerprint scanners to secure authentication during login attempts and prevent credential stuffing and brute-force attacks.

#7. Network and Infrastructure Security

A behavior-oriented approach can be deployed to analyze network traffic patterns, detect unusual activities, and identify potential attacks like distributed denial-of-service (DDoS) attacks or intrusion attempts. 

#8. Incident Response and Forensics

Artificial Intelligence in cybersecurity can aid in automating incident response processes by analyzing and correlating security event data, identifying the root cause of incidents, and suggesting remediation actions. It can also assist in digital forensics investigations by processing large volumes of data and uncovering hidden patterns. 

#9. WAF with AI 

WAFs are increasingly becoming part of the application security strategy. Web applications and API protection (WAAP) provide security from the edge of the database. 

Use of AI in Cybercrime 

With rapid technological advancements, such as AI in cybersecurity, organizations need to navigate the ever-changing environment. However, it poses various challenges for businesses in their pursuit of ensuring the security of their systems and data. Here are some key challenges: 

#1. Cloud Adoption

Organizations are leveraging multiple cloud providers for their business needs. While it offers flexibility, it also attracts complexity. It increases the number of platforms and interaction points an application has to provide consistency across, ensuring it does not create an increasing number of vulnerabilities. 

#2. API Integrations

The integration of systems and services through APIs has become prevalent. However, this also makes APIs a prime target for hackers. Therefore, protecting data ‘in transit’ and ‘in use’ has become a significant concern for organizations. 

#3. AI in Cyber Attacks

Artificial intelligence (AI) and machine learning (ML) methods are increasingly adopted in cyber attacks. AI supports the establishment of covert channels to siphon sensitive information. Therefore, these technologies enable cyber attackers to enhance their methods and tactics, making it more challenging for traditional security measures to spot and prevent their activities. 

#4. AI in Malware 

Malware creators increasingly deploy AI and ML methods to enhance the capabilities of their attacks. They can incorporate new, sophisticated, and changing features and functions on the fly, creating AI-driven malware that can continually adapt and evade traditional defense mechanisms. Therefore, AI in cybersecurity will play a vital role to combat these scenarios. 

#5. AI-Driven Threats 

The evolving environment presents a host of other threats that gain momentum by leveraging AI. These include targeted attacks against AI detection functions, advanced payload obfuscation techniques, evasion of networked communication with AI methods, decentralized botnet control using swarm intelligence, and the concealment of malware payloads within neural networks. 

How Robosoft Helps Build an Environment of Trust

Robosoft helps its customers tackle cyber threats through two approaches:   

  • Risk-based Approach: The National Institute of Standards and Technology (NIST) adopts a risk-oriented approach to reduce cybersecurity risks to networks and data. Domain-specific frameworks such as HIPAA or standards such as PCI/DSS are woven into the approach. 
  • Compliance-based Approach: Frameworks such as ISO 27001 list 14 security control areas and 114 controls against which projects can be deployed to build an effective shield against threats and be security compliant. 

At Robosoft, we provide a wide range of cybersecurity services to address security challenges. We help assess:

  • Environment Risk, Cybersecurity Maturity, and Security Architecture 
  • Formulate operating policies and procedures. 
  • Evaluate security vendors and tools. 
  • Program management of the cybersecurity program 

In addition, Robosoft helps deploy the selected tool across the complex organizational landscape. We specialize in identifying security tools that provide spherical (not just 360°) security and deploying them to keep threats at bay and meet compliance requirements. 

Furthermore, Robosoft has a systematic methodology for each area of security and an overarching security program. 

The Future of AI in Cybersecurity 

At Robosoft, we believe AI is here to become an intrinsic part of cybersecurity, ushering in a transformative era. As the landscape of digital threats becomes increasingly complex and insidious, our vision for the future of AI in cybersecurity embodies a significant change from a reactive approach to a proactive one. 

Traditionally, cybersecurity played a follower role through a reactionary force in post-attack solution development. However, as we progress to behavior and pattern-based defense, particularly through the use of AI in cybersecurity, security solutions will be able to steal a march over threats, enabling faster and more accurate responses to cyber threats. 

  • Authentication techniques will get more sophisticated, incorporating facial detection, biometrics, and such solutions along with behavioral analysis and anomaly detection.
  • Vulnerability detection will not be limited to just scanning and identifying weaknesses but will include real-time remediation.
  • Continuous monitoring and analysis of vast amounts of data will enhance the Intelligence of Intrusion Detection Systems (IDS) by incorporating behavior and pattern-based approaches. It will enable them to adapt to emerging threats and significantly improve their response accuracy and speed.
  • Adversarial AI will be leveraged to feed manipulated data to malicious actors that, too, leverage AI to launch sophisticated attacks, thereby evading detection. Nevertheless, machine learning algorithms will continuously evolve to counter such AI-based threats.

Security threats will persist and get more sophisticated over time. With AI in cybersecurity, we have an effective countermeasure against such threats.

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