How Artificial Intelligence Is Shaping the Future of Cybersecurity

  • June 13, 2023

Emily Bonnie

Senior Content Marketing Manager at Secureframe


Anna Fitzgerald

Senior Content Marketing Manager at Secureframe

In both positive and negative ways, AI is shaping the present and future of cybersecurity.

Today, threat actors are manipulating ChatGPT to generate malware, pinpoint vulnerabilities in code, and bypass user access controls. Social engineers are leveraging AI to launch more precise and convincing phishing schemes and deepfakes. Hackers are using AI-supported password guessing and CAPTCHA cracking to gain unauthorized access to sensitive data. 

Yet AI, machine learning, predictive analytics, and natural language processing are also being used to strengthen cybersecurity in unprecedented ways — flagging concealed anomalies, identifying attack vectors, and automatically responding to security incidents. 

To fully grasp the shifting threat landscape, improve security postures, and thwart attacks, cybersecurity and IT leaders must understand the benefits and risks of artificial intelligence. 

How artificial intelligence is improving cybersecurity

Despite headlines being dominated by weaponized AI, artificial intelligence is a powerful tool for organizations to enhance their security posture. Algorithms capable of analyzing massive amounts of data make it possible to quickly identify threats and vulnerabilities, mitigate risks, and prevent attacks. 

1. Identifying attack precursors

AI algorithms, particularly ML and deep learning models, can analyze massive volumes of data and identify patterns that human analysts might miss. This ability will facilitate early detection of threats and anomalies, preventing security breaches and allowing systems to become proactive rather than reactive.

AI systems can be trained to run pattern recognition and detect malware or ransomware attacks before they enter the system. Predictive intelligence paired with natural language processing can scrape news, articles, and studies on emerging cyber threats and cyberattack trends to curate new data, improve functionality, and mitigate risks before they materialize into full-scale attacks.

2. Strengthening access control and password practices

AI enhances access control and password practices by employing advanced authentication mechanisms. Biometric authentication such as facial recognition or fingerprint scanning can strengthen security measures by reducing reliance on traditional passwords. 

AI algorithms can also analyze login patterns and behaviors to identify minor behavioral anomalies and suspicious login attempts, allowing organizations to mitigate insider threats and address potential security breaches faster. 

3. Minimizing and prioritizing risks

The attack surface for modern enterprises is massive, and growing every day. The ability to analyze, maintain, and improve such a significant vulnerability landscape now requires more than humans alone can reasonably achieve. 

As threat actors capitalize on emerging technologies to launch progressively sophisticated attacks, traditional software and manual techniques simply can’t keep up. 

Artificial intelligence and machine learning are quickly becoming essential tools for information security teams to minimize breach risk and bolster security by identifying vulnerabilities in systems and networks. Machine learning models can scan infrastructure, code, and configurations to uncover weaknesses that could be exploited by attackers. By proactively identifying and patching vulnerabilities, organizations can significantly reduce the risk of successful cyberattacks.

By leveraging machine learning algorithms, organizations can automate risk assessments and allocate resources effectively. AI can provide insights into the likelihood and consequences of different types of attacks, enabling cybersecurity teams to prioritize mitigation efforts efficiently.

In other words, AI-based cybersecurity systems can prioritize risks based not only on what cybercriminals could use to attack your systems, but on what they’re most likely to use to attack your systems. Security and IT leadership can better prioritize and allocate resources to the highest vulnerabilities. 

4. Automating threat detection & response

With AI, cybersecurity systems can not only identify but also respond to threats automatically. 

  • Malicious IP addresses can be blocked automatically 
  • Compromised systems or user accounts can be shut down immediately
  • ML algorithms can analyze emails and web pages to identify and block potential phishing attempts

AI-powered systems automate threat detection processes, providing real-time monitoring and rapid response. Machine learning algorithms continuously analyze network traffic, user behavior, and system logs to identify suspicious activities. By leveraging AI's ability to process and analyze massive volumes of data, organizations can detect and respond to threats immediately, minimizing the time window for attackers to exploit vulnerabilities.

Intelligent algorithms can analyze security alerts, correlate events, and provide insights to support decision-making during an incident. AI-powered incident response platforms can automate investigation workflows, rapidly identify the root cause of an incident, and suggest appropriate remedial actions. These capabilities empower security teams to respond quickly, minimizing the impact of security breaches.

5. Increasing human efficiency & effectiveness

82% of security breaches involve human error. By automating routine manual tasks, AI can play a pivotal role in reducing the likelihood of misconfigurations, accidental data leaks, and other inadvertent mistakes that could compromise security.

AI also equips cybersecurity teams with powerful tools and insights that improve their efficiency and effectiveness. Machine learning models can analyze vast amounts of threat intelligence data, helping teams more fully understand the threat landcape and stay ahead of emerging threats. 

AI-powered security and compliance automation platforms streamline workflows, enabling teams to respond to incidents faster and with greater precision. By offloading time-consuming manual tasks, cybersecurity professionals can focus on strategic initiatives and higher-level threat analysis.

From predictive analytics to automated threat detection and incident response, AI augments the capabilities of cybersecurity teams, enabling proactive defense measures. Embracing AI technology empowers organizations to stay ahead in the cybersecurity landscape and safeguard their valuable assets.

The challenges and risks of implementing AI in cybersecurity

Cybersecurity leaders that are looking to implement AI to enhance their security posture must address a range of challenges and risks first, including those related to transparency, privacy, and security. 

Data privacy concerns

AI systems often require large amounts of data, which can pose privacy risks. If AI is used for user behavior analytics, for example, it may need access to sensitive personal data.

Where does AI data reside? Who can access it? What happens when the data is no longer needed? More companies are walking a tightrope to balance user privacy with data utility. 

Proper AI governance is foundational to minimizing financial and reputational risk. Over the coming years, there will be an increased demand for effective ways to monitor AI performance, detect stale models or biased results, and make the proper adjustments. 

Organizations will need to adopt an AI governance approach that encompasses the entire data lifecycle, from data collection to processing, access, and disposal. Privacy by design will need to become a greater focus in the AI lifecycle and in AI governance strategies, including data anonymization techniques that preserve user privacy without impacting data’s usefulness for AI applications. 

Reliability and accuracy

While AI systems can process vast amounts of data quickly, they are not perfect. False positives and negatives can occur, potentially leading to overlooked threats or unnecessary alarm.

In addition, AI and ML algorithms are only as good as the data they ingest. Organizations will need to invest in data preparation processes to organize and clean data sets to ensure integrity and accuracy and avoid false positives. 

Lack of transparency

AI systems, especially deep learning models, often function as black boxes, making it challenging to understand how they arrive at specific decisions or predictions. This lack of transparency creates a barrier for cybersecurity experts who need to understand the reasoning behind an AI system's outputs, particularly when it comes to identifying and mitigating security threats. Without transparency, it becomes difficult to trust the decisions made by AI systems and validate their accuracy.

In addition, AI systems may generate false positives, overwhelming security teams in constantly putting out fires. False negatives can result in missed threats and compromised security. Lack of transparency into the reasons for these errors makes it difficult to fine-tune AI models, improve accuracy, and rectify any real issues. Cybersecurity experts need to be able to understand and validate the decisions made by AI systems to effectively defend against evolving cyber threats.

How cybersecurity leaders can successfully incorporate AI into their security programs

As the role of artificial intelligence in cybersecurity continues to grow, cybersecurity leaders play a critical role in harnessing the potential of AI while ensuring its secure and effective implementation. By following these best practices, cybersecurity leaders can effectively implement AI while addressing concerns related to transparency, privacy, and security. 

1. Align AI strategy with business & security objectives

Before embarking on AI implementation, cybersecurity leaders must align AI strategy with the organization's broader business and security objectives. Clearly define the desired outcomes, identify the specific cybersecurity challenges AI can address, and ensure that AI initiatives align with the organization's overall security strategy.

2. Invest in skilled AI talent

While AI can significantly enhance a cybersecurity system, it should not replace human expertise. Building an AI-ready cybersecurity team is crucial. 

Invest in recruiting information security professionals who understand AI technologies. By having a team with the right expertise, you can effectively evaluate AI solutions, implement them, and continuously optimize their performance. Cybersecurity leaders should promote AI literacy within their organizations to help team members use AI tools effectively and understand their limitations.

3. Thoroughly evaluate AI solutions

Take a diligent approach when evaluating AI solutions. Assess the vendor's reputation, the robustness of their AI models, and their commitment to cybersecurity and privacy. Conduct thorough proof-of-concept trials and evaluate how well the solution integrates with existing cybersecurity infrastructure. Ensure that the AI solution aligns with your organization's security requirements and regulatory obligations.

4. Establish a robust data governance framework

AI relies on high-quality, diverse, and well-curated data. Establish a robust data governance framework that ensures data quality, integrity, and privacy. Develop processes for collecting, storing, and labeling data while adhering to relevant regulations. Implement measures to protect data throughout its lifecycle and maintain strict access controls to safeguard sensitive information. Choose AI models that are explainable, interpretable, and can provide insights into their decision-making processes. 

5. Implement strong security measures for AI infrastructure

Ensure the security of AI infrastructure by implementing robust security measures. Apply encryption to sensitive AI model parameters and data during training, deployment, and inference. Protect AI systems from unauthorized access and tampering by implementing strong authentication mechanisms, secure APIs, and access controls. Regularly patch and update AI frameworks and dependencies to address security vulnerabilities.

Embracing the future of cybersecurity

Artificial intelligence and machine learning are set to play an increasingly pivotal role in information security, presenting cybersecurity leaders with both opportunities and challenges. 

While a good deal of uncertainty surrounds artificial intelligence and cybersecurity, one thing is clear: it will have a tremendous impact on how cybersecurity leaders safeguard their organizations and the threats they face.

There is incredible potential for AI to empower IT and infosec professionals, drive progress, and improve information security practices for organizations of all sizes.

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