Artificial intelligence (AI) has been a game-changer in cybersecurity. It has revolutionized the approach to cybersecurity by providing advanced techniques to detect and mitigate cyber threats.

The use of AI in cybersecurity is increasing rapidly, with many companies adopting it as a key tool in their cybersecurity strategy.

According to a report by MarketsandMarkets, the global AI in cybersecurity market size is expected to grow from $8.8 billion in 2020 to $38.2 billion by 2026, at a CAGR of 23.3% during the forecast period.

The report also highlights the increasing need for AI in cybersecurity due to the rising number of cyber threats and the shortage of skilled cybersecurity professionals.

Here's what we'll cover in this article:

  1. The traditional approach to cybersecurity before AI
  2. How AI is different from traditional approaches
  3. How AI is used in cybersecurity
  4. How AI is changing the cybersecurity landscape
  5. Challenges associated with using AI in cybersecurity
  6. Conclusion

The Traditional Approach to Cybersecurity Before AI Was Introduced

Before the advent of AI, traditional cybersecurity relied heavily on signature-based detection systems. These systems worked by comparing incoming traffic to a database of known threats or malicious code signatures. When a match was found, the system would trigger an alert and take action to block or quarantine the threat.

While this approach was effective against known threats, it was inadequate against new and unknown threats. Cybercriminals could easily bypass signature-based detection systems by modifying the code or creating new variants of malware that were not yet in the database.

Signature-based detection systems could generate a high number of false positives, as legitimate traffic could be flagged as malicious if it happened to share similar characteristics to a known threat. This led to security analysts spending a significant amount of time investigating false positives, which could be a drain on resources.

Traditional cybersecurity also relied on manual analysis. Security analysts would manually investigate security alerts and logs, looking for patterns or indicators of a security breach. This process was time-consuming and often relied on the expertise of the security analyst to identify threats.

Rule-based systems worked by setting up rules or policies that defined acceptable behavior on a network. If traffic violated these rules, it would trigger an alert. While rule-based systems could be effective in certain situations, they were often inflexible and could not adapt to new and emerging threats.

The traditional approach to cybersecurity before AI was introduced was largely reactive, relying on manual analysis, signature-based detection systems, and rule-based systems. This approach was often ineffective against new and unknown threats, and it could generate a high number of false positives, which could be a drain on resources.

How AI is Different From Traditional Approaches to Cybersecurity

AI-based solutions in cybersecurity differ from traditional approaches in several ways.

As we just discussed, traditional approaches to cybersecurity relied heavily on signature-based detection systems that were only effective against known threats. This meant that new and unknown threats could go undetected.

In contrast, AI-based solutions use machine learning algorithms that can detect and respond to both known and unknown threats in real-time.

Machine learning algorithms are trained using vast amounts of data, including historical threat data and data from the network and endpoints, to identify patterns that are difficult for humans to see. This allows AI-based solutions to identify and respond to threats in real-time, without the need for human intervention.

For example, machine learning algorithms can analyze network traffic patterns to identify anomalous behavior that may indicate a cyberattack, and then alert security personnel or even take automated action to mitigate the threat.

Another way that AI-based solutions differ from traditional approaches is that they are designed to continuously learn and adapt.

As new threats emerge, machine learning algorithms can be trained on new data to improve their ability to detect and respond to these threats. This means that AI-based solutions can keep pace with the evolving threat landscape and provide more effective cybersecurity protection over time.

The use of AI in cybersecurity represents a major shift in how organizations approach cybersecurity. AI-based solutions can provide more effective protection against both known and unknown threats – using machine learning algorithms to detect and respond to threats in real-time. This helps organizations to better safeguard their sensitive data and critical systems.

How AI is Used in Cybersecurity

AI is being used in cybersecurity to detect and respond to cyber threats in real-time. AI algorithms can analyze large amounts of data and detect patterns that are indicative of a cyber threat.

Malware Detection

Malware is a significant threat to cybersecurity. Traditional antivirus software relies on signature-based detection to identify known malware variants.

Signature-based detection is a technique that compares a file to a database of known malware signatures and detects a match. This technique is only effective against known malware variants, and it can be easily bypassed by malware that has been modified to evade detection.

AI-based solutions use machine learning algorithms to detect and respond to both known and unknown malware threats. Machine learning algorithms can analyze large amounts of data to identify patterns and anomalies that are difficult for humans to detect. By analyzing the behavior of malware, AI can identify new and unknown malware variants that may be missed by traditional antivirus software.

AI-based malware detection solutions can be trained using both labeled and unlabeled data.

Labeled data refers to data that has been tagged with specific attributes, such as whether a file is malicious or benign. Unlabeled data, on the other hand, is not tagged and can be used to train the machine learning algorithms to identify patterns and anomalies in data.

AI-based malware detection solutions can use various techniques to identify malware, such as static analysis and dynamic analysis.

Static analysis involves analyzing the characteristics of a file, such as its size, structure, and code, to identify patterns and anomalies. Dynamic analysis involves analyzing the behavior of a file when it is executed to identify patterns and anomalies.

AI-based solutions provide a more advanced and effective approach to malware detection than traditional antivirus software. They can identify new and unknown malware variants that may be missed by traditional antivirus software.

Phishing Detection

Phishing is a prevalent form of cyber-attack that targets individuals and organizations.

Traditional phishing detection approaches typically rely on rules-based filtering or blacklisting to identify and block known phishing emails. These approaches have limitations because they are only effective against known attacks and may miss new or evolving attacks.

AI-based phishing detection solutions use machine learning algorithms to analyze the content and structure of emails to identify potential phishing attacks. These algorithms can learn from vast amounts of data to detect patterns and anomalies that indicate a phishing attack.

AI-based solutions can also analyze the behavior of users when interacting with emails to identify potential phishing attacks. For example, if a user clicks on a suspicious link or enters personal information in response to a phishing email, AI-based solutions can flag that activity and alert security teams.

Security Log Analysis

Traditional security log analysis relies on rule-based systems that are limited in their ability to identify new and emerging threats.

AI-based security log analysis uses machine learning algorithms that can analyze large volumes of security log data in real-time.

AI algorithms can detect patterns and anomalies that may indicate a security breach, even in the absence of a known threat signature. Organizations can then quickly identify and respond to potential security incidents, reducing the risk of data breaches and other security incidents.

AI-based security log analysis can also help organizations identify potential insider threats. By analyzing user behavior across multiple systems and applications, AI algorithms can detect anomalous behavior that may indicate insider threats, such as unauthorized access or unusual data transfers. Organizations can then take action to prevent data breaches and other security incidents before they occur.

AI-based security log analysis provides organizations with a powerful tool for identifying potential threats and taking action to mitigate them.

Network Security

AI algorithms can be trained to monitor networks for suspicious activity, identify unusual traffic patterns, and detect devices that are not authorized to be on the network.

AI can improve network security through anomaly detection. This involves analyzing network traffic to identify patterns that are outside the norm. By analyzing historical traffic data, AI algorithms can learn what is normal for a particular network and identify traffic that is anomalous or suspicious. This can include unusual port usage, unusual protocol usage, or traffic from suspicious IP addresses.

AI can also improve network security by monitoring devices on the network. AI algorithms can be trained to detect devices that are not authorized to be on the network and alert security teams to potential threats.

For example, if a new device is detected on the network that has not been authorized by the IT department, the AI system can flag it as a potential security risk. AI can also be used to monitor the behavior of devices on the network, such as unusual patterns of activity, to detect potential threats.

Endpoint Security

Endpoints, such as laptops and smartphones, are often targeted by cybercriminals. Traditional antivirus software relies on signature-based detection, which can only detect known malware variants. AI can detect unknown malware variants by analyzing their behavior.

AI-based endpoint security solutions use machine learning algorithms to analyze endpoint behavior and detect potential threats.

For example, an AI-based endpoint security solution can scan files for malware and quarantine any suspicious files. It can also monitor endpoint activity and detect unusual behavior that may indicate a security threat.

AI-based endpoint security solutions can also block unauthorized access attempts and prevent attackers from gaining access to sensitive data.

One key advantage of AI-based endpoint security solutions is their ability to adapt and evolve over time. As cyber threats evolve and become more sophisticated, AI algorithms can learn from new data and identify new patterns that indicate potential threats. This means that AI-based endpoint security solutions can provide better protection against new and unknown threats than traditional antivirus software.

AI-based endpoint security solutions provide real-time protection. AI algorithms can analyze endpoint behavior in real-time and alert security teams to potential threats. This means that security teams can respond to threats more quickly and prevent them from causing damage.

How AI is Changing the Cybersecurity Landscape

There are many benefits to using AI in cybersecurity.

Increased Efficiency

AI frees up security analysts to focus on more complex and critical tasks, such as incident response and threat hunting, by automating routine tasks

AI enhances efficiency in the analysis of large volumes of security data. Security analysts often face the challenge of sifting through extensive logs, alerts, and reports to identify potential threats. AI algorithms can rapidly process and analyze vast amounts of data, detecting patterns and anomalies that may indicate a cyber threat. This helps security teams identify and prioritize potential risks more efficiently.

AI-powered automation also plays a crucial role in tasks like vulnerability scanning and patch management. AI can automatically scan systems and networks for vulnerabilities, identifying potential weaknesses that may be exploited by attackers. It can then prioritize and recommend patches or security updates, streamlining the patch management process.

This automation reduces the time and effort required by security analysts to manually identify vulnerabilities and apply patches, allowing them to focus on critical security issues.

AI can contribute to streamlining incident response processes. When a security incident occurs, AI algorithms can help assess the severity and impact of the incident by analyzing relevant data. They can provide real-time alerts and recommendations, enabling security teams to respond promptly and effectively.

AI can also assist in automating incident investigation and forensics, accelerating the identification of the root cause and aiding in remediation efforts.

Improved Accuracy

AI algorithms excel at detecting threats that may be challenging for humans to identify, including new and unknown malware variants, as well as subtle patterns in network traffic that indicate a potential cyber threat.

AI demonstrates its accuracy in the detection of new and emerging malware. Traditional signature-based antivirus software relies on a database of known malware signatures to identify threats. But this approach is limited to detecting only known malware variants. AI utilizes advanced machine learning algorithms to analyze the behavior of files and programs, allowing it to detect new and unknown malware variants.

AI algorithms can flag suspicious files and applications even if they do not match any known malware signatures by identifying patterns of malicious behavior. This capability provides organizations with enhanced protection against evolving and sophisticated cyber threats.

AI algorithms can analyze network traffic to identify patterns that indicate a potential cyber threat. AI can detect anomalies, unusual traffic patterns, or suspicious behaviors that may go unnoticed by human analysts by processing large volumes of network data.

For instance, AI algorithms can identify communication with known malicious IP addresses, detect port scanning activities, or recognize unauthorized data exfiltration attempts.

The accuracy of AI in cybersecurity is further amplified by its ability to continuously learn and adapt. Machine learning algorithms can be trained on vast datasets that encompass diverse threat scenarios and behaviors, enabling them to improve their detection capabilities over time.

As AI algorithms learn from new data, they can refine their models and identify emerging threat patterns with increased accuracy.

This adaptive nature of AI allows organizations to stay ahead of evolving cyber threats and significantly enhances the accuracy of their cybersecurity defenses.

Reducing Costs

Organizations can achieve cost savings in multiple areas of their cybersecurity operations by leveraging AI-powered automation and improving the accuracy of threat detection.

AI reduces costs through task automation. Many routine and repetitive tasks that were traditionally performed by human analysts can now be automated using AI algorithms. This includes activities such as log analysis, routine vulnerability assessments, and patch management.

Organizations can significantly reduce the need for manual intervention, thereby reducing the workload and associated costs of human resources. AI automation allows for faster and more efficient execution of these tasks, resulting in operational efficiency gains and cost savings.

AI's ability to improve the accuracy of threat detection also contributes to cost reduction. Traditional security approaches often generate false positives or miss certain types of threats due to limitations in detection mechanisms. This can lead to wasted time and resources investigating false alarms or, worse, missing actual security incidents.

AI algorithms, by leveraging advanced analytics and machine learning, can analyze vast amounts of data and detect patterns that may indicate a cyber threat more accurately.

By reducing false positives and improving detection rates, organizations can streamline their incident response processes, allocate resources more effectively, and avoid unnecessary costs associated with false alarms or undetected breaches.

Another way AI can aid in cost reduction is by enhancing the efficiency of incident response and reducing the time to remediate security incidents. AI algorithms can swiftly analyze and correlate data from various sources, enabling faster incident triage and response.

This rapid response time minimizes the potential impact of a security breach and reduces the associated costs, such as financial losses, reputational damage, and regulatory penalties.

AI can also contribute to cost reduction in the realm of proactive threat intelligence. AI-powered algorithms can continuously monitor and analyze global threat intelligence feeds, dark web forums, and other relevant sources to identify emerging threats and vulnerabilities.

This allows organizations to proactively address potential risks, prioritize their security efforts, and allocate resources efficiently. This, in turn, results in cost savings associated with incident prevention and mitigation by obtaining timely and actionable threat intelligence.

Real-Time Threat Detection and Response

In the fast-paced and constantly evolving landscape of cyber threats, the ability to detect and respond to attacks in real-time is essential to minimize the potential damage caused by malicious activities.

By processing data from various sources rapidly, AI can identify suspicious patterns, anomalies, or indicators of compromise that may signify an ongoing or imminent cyber attack. This real-time analysis allows security teams to gain immediate visibility into potential threats and take prompt action to mitigate risks.

Machine learning algorithms can be trained on historical data, allowing them to recognize known attack patterns and behaviors. As new threats emerge, AI algorithms can dynamically adjust their detection models, ensuring that they stay up-to-date with the evolving threat landscape.

This adaptability enables AI to identify emerging and previously unseen threats in real-time, providing organizations with proactive defense capabilities.

When a potential threat is detected, AI-powered systems can trigger real-time alerts and notifications to security teams, enabling them to respond swiftly. These alerts can include detailed information about the nature of the threat, its potential impact, and recommended remediation actions.

AI empowers security teams to make informed decisions and respond effectively to mitigate the risks associated with cyber attacks by providing actionable insights in real-time.

AI can also automate certain aspects of the response process, such as isolating affected systems, blocking malicious activities, or initiating incident response workflows.

Organizations can minimize the time between threat detection and response, reducing the window of opportunity for attackers and limiting the potential impact of a security incident by automating these response actions.

Real-time threat detection and response offered by AI is particularly valuable in preventing data breaches, minimizing financial losses, and safeguarding organizational reputation.

By swiftly detecting and neutralizing threats, organizations can minimize the dwell time of attackers within their networks, reducing the likelihood of data exfiltration, system compromise, or unauthorized access.

Real-time response capabilities also enable security teams to contain and eradicate threats before they spread, preventing further damage and disruption.

Improved Scalability

Traditional cybersecurity approaches often face challenges when it comes to handling large volumes of data and maintaining efficient operations in complex environments. AI excels in scalability, enabling organizations to effectively analyze massive amounts of data and respond to cyber threats efficiently.

AI algorithms are designed to process and analyze vast datasets, including network traffic logs, system logs, user behaviors, and threat intelligence feeds. AI algorithms can identify patterns, anomalies, and indicators of cyber threats within these extensive datasets.

The scalability of AI allows it to handle the increasing volumes of data generated in modern digital ecosystems, including cloud environments, IoT devices, and interconnected networks.

The ability of AI to scale effectively is particularly valuable in dynamic and rapidly evolving cybersecurity landscapes. As the volume and complexity of data continue to grow, traditional approaches may struggle to keep pace.

With AI, organizations can leverage its inherent scalability to process and analyze data in real-time, ensuring that cyber threats are promptly detected and addressed.

One area where scalability is crucial is threat detection. AI algorithms can process massive volumes of data from various sources simultaneously, enabling them to detect subtle patterns and indicators of cyber threats that may go unnoticed by traditional systems.

AI can identify sophisticated attack techniques, emerging threats, and zero-day vulnerabilities. This empowers organizations to take proactive measures to counter potential risks by analyzing vast amounts of data rapidly.

AI's scalability extends to response capabilities. When a threat is detected, AI-powered systems can generate real-time alerts and initiate response actions across an organization's infrastructure.

The scalability of AI allows for coordinated responses across multiple endpoints, systems, and networks, ensuring that threats are effectively contained and mitigated.

Organizations can achieve improved operational efficiency in cybersecurity by harnessing AI's scalability. The ability to analyze large datasets efficiently reduces the time required for threat detection and response. This enables security teams to focus on critical tasks and make informed decisions promptly.

With AI's scalable capabilities, organizations can optimize resource allocation, improve incident response times, and effectively protect their digital assets against evolving cyber threats.

It is important to note that while AI brings enhanced scalability to cybersecurity, it should be complemented by human expertise. AI algorithms can process vast amounts of data and identify potential threats, but human analysts play a crucial role in interpreting the results, validating findings, and making informed decisions.

The combination of AI's scalability and human intelligence creates a powerful synergy in cybersecurity operations, enabling organizations to stay ahead of threats and protect their assets effectively.

Challenges Associated With Using AI in Cybersecurity

While there are many benefits to using AI in cybersecurity, there are also potential risks that must be considered.


Bias refers to the systematic and unfair favoritism or discrimination in the outcomes produced by an algorithm. In the context of cybersecurity, bias can result in false positives or false negatives, leading to flawed decisions, missed threats, or unjust actions.

Bias in AI algorithms stems from the data used to train them. If the training data is biased or unrepresentative, the AI algorithm will learn and perpetuate those biases in its predictions and decisions.

For example, if an AI algorithm is trained on a dataset that predominantly consists of emails from male senders, it may inadvertently flag emails from female senders as spam at a higher rate, assuming a biased association between gender and spam content.

The cybersecurity community can strive towards fairness, transparency, and equity by actively addressing bias in AI algorithms. This involves a collective effort from AI developers, cybersecurity practitioners, regulators, and stakeholders to ensure that AI-driven cybersecurity solutions are unbiased, reliable, and trustworthy.

While AI brings numerous benefits to cybersecurity, the risk of bias should not be overlooked.

To mitigate bias, it is essential to focus on diverse and representative training data, rigorous preprocessing and cleaning techniques, ongoing monitoring and evaluation, explainability and transparency, ethical considerations, and continuous education.

Organizations can develop AI algorithms that enhance cybersecurity without compromising fairness and equality.

Malicious Use

Attackers can leverage AI technologies to enhance the sophistication and effectiveness of their cyber attacks, posing significant challenges for defensive measures.

AI-Enhanced Phishing Attacks: Phishing attacks involve the use of deceptive techniques to trick individuals into divulging sensitive information or performing malicious actions. AI can be harnessed by attackers to create highly convincing and personalized phishing emails.

AI can generate content that closely mimics legitimate communications, making it harder for users to discern between genuine and fraudulent messages by employing natural language processing (NLP) and machine learning algorithms. These AI-generated phishing emails may evade traditional email filters and increase the success rate of attacks.

Advanced Evasion Techniques: AI-powered evasion techniques can enable cybercriminals to circumvent traditional security defenses and remain undetected. Attackers can develop malware that dynamically modifies its behavior to evade AI-based detection systems.

Malware can adapt its characteristics and signatures to bypass existing security controls. This makes it more challenging for security solutions to identify and neutralize these threats by employing generative adversarial networks (GANs) or reinforcement learning.

Automated Attack Tools: AI can automate various stages of the cyber attack lifecycle, making it easier for attackers to scale their operations and target a larger number of victims.

For instance, AI algorithms can automate the process of reconnaissance, vulnerability scanning, and even exploit selection. Adversaries can efficiently identify vulnerabilities, launch targeted attacks, and exploit weaknesses in security systems by using AI-driven attack tools.

Deepfake Attacks: Deepfake technology, powered by AI, allows the creation of highly realistic synthetic media, such as images, audio, and videos. This can be exploited by threat actors to deceive individuals or manipulate information.

Deepfake attacks can be used to fabricate compromising or misleading content, impersonate high-profile individuals, or spread disinformation, leading to reputational damage, financial loss, or societal upheaval.

Adversarial Attacks: Adversarial attacks aim to manipulate or deceive AI systems by exploiting vulnerabilities in their design or input data. Adversaries can generate specifically crafted inputs to fool AI models into making incorrect predictions or decisions.

For example, an attacker could alter certain features of an image, making it indistinguishable to humans but causing an AI-powered security system to misclassify it as benign instead of malicious.

How to mitigate these risks

To mitigate the risks associated with the malicious use of AI in cybersecurity, consider implementing several security measures:

  • Ethical Guidelines and Regulation: The development and deployment of AI technologies in cybersecurity should adhere to ethical guidelines and industry best practices. Regulatory frameworks can provide oversight and ensure responsible use of AI, mitigating the risks associated with its malicious use.
  • Human Oversight and Decision-making: While AI can automate certain cybersecurity tasks, human expertise and judgment remain crucial. Incorporating human oversight in critical decision-making processes can help prevent AI systems from being exploited or making flawed judgments solely based on machine-driven decisions.
  • Collaboration and Information Sharing: Effective collaboration among cybersecurity professionals, researchers, and industry stakeholders is vital to stay ahead of evolving AI-driven threats. Sharing knowledge, best
    practices, and threat intelligence can enable the collective defense against malicious AI-based attacks. Public-private partnerships and information-sharing platforms can facilitate such collaborations and foster a more robust cybersecurity ecosystem.
  • Responsible Data Governance: To mitigate bias and ensure fairness in AI algorithms, organizations must adopt responsible data governance practices. This involves ensuring diverse and representative datasets for training AI models, implementing data anonymization techniques to protect user privacy, and regularly auditing and monitoring data sources for potential biases.
  • AI System Transparency and Explainability: Enhancing the transparency and explainability of AI systems is crucial to detect and address potential biases or vulnerabilities. Organizations should strive to develop AI models and algorithms that provide clear explanations for their decisions and actions, enabling security analysts to validate the system's outputs and identify any potential malicious manipulation.
  • Ongoing Research and Innovation: Continued research and innovation in AI and cybersecurity are vital to stay ahead of emerging threats. Advancements can be made in developing robust AI-driven security solutions, detecting and mitigating AI-driven attacks, and addressing the potential risks associated with malicious AI use by fostering collaboration between academia, industry, and government agencies.

Proactive defense strategies, combined with ongoing vigilance, collaboration, and responsible AI development practices, can help ensure the safe and effective utilization of AI technologies to bolster cybersecurity defenses.

Security Vulnerabilities

Just like any other software or system, AI-powered security solutions can have vulnerabilities that attackers can exploit for their malicious purposes. These vulnerabilities can enable attackers to bypass or manipulate AI algorithms, compromising the effectiveness of the cybersecurity measures.

To address and mitigate the risks associated with security vulnerabilities in AI systems, organizations should consider the following measures:

  • Regular Security Assessments: Conduct regular security assessments and penetration testing of AI systems to identify and address potential vulnerabilities. These assessments should simulate real-world attacks and attempt to exploit weaknesses in the AI system's infrastructure, algorithms, or data handling processes.
  • Secure Development Practices: Incorporate secure development practices from the early stages of AI system development. This includes adhering to secure coding standards, conducting thorough security assessments, and employing secure development frameworks and tools.
  • Secure Deployment and Configuration: Implement secure deployment and configuration practices for AI systems. This includes properly configuring access controls, securely storing sensitive data used by the AI system, and implementing secure communication protocols. Additionally, organizations should regularly update and patch AI systems to address any known security vulnerabilities.
  • Ongoing Monitoring and Incident Response: Continuously monitor the AI system for any unusual or suspicious activities that may indicate a security breach. Implement robust logging and monitoring mechanisms to track system behavior, detect anomalies, and respond promptly to any security incidents. Establish an incident response plan to guide the organization's actions in the event of a security breach or vulnerability exploit.
  • Vendor Evaluation and Security Considerations: When adopting AI systems from third-party vendors, conduct thorough security evaluations to ensure that the vendor follows secure development practices and has robust security measures in place. Consider security as a crucial criterion when selecting AI solutions, and engage in dialogue with vendors to address any security concerns or questions.


The increasing use of artificial intelligence (AI) in cybersecurity presents a transformative opportunity to enhance the effectiveness and efficiency of security measures.

AI brings a range of capabilities that can revolutionize the traditional approach to cybersecurity. AI has the potential to significantly strengthen our defense against evolving cyber threats by automating tasks, improving accuracy, and reducing costs.

The adoption of AI in cybersecurity enables organizations to detect and respond to threats in real-time, leveraging machine learning algorithms that can analyze vast amounts of data and identify patterns that are difficult for humans to discern.

This real-time threat detection and response capability is particularly crucial in today's fast-paced cybersecurity landscape, where threats can emerge and evolve rapidly.

AI holds immense potential to revolutionize the field of cybersecurity and organizations can leverage AI effectively to bolster their security posture and stay ahead in the ever-evolving landscape of cybersecurity. But it is crucial to approach AI adoption with a thorough understanding of the associated risks and implement appropriate measures to mitigate them.

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