Understand the key differences between cyber-security and artificial intelligence to know how the two benefit each other, how AI systems may cause more risks, and how businesses can develop cyber-security systems with advanced technology that works in their favor with minimal risks. AI is both the greatest threat and defense for cybersecurity, and knowing how to use AI in cybersecurity matters.
AI in cybersecurity already achieved a market value of $30+ billion in 2024 while experts project an increase to $134 billion by 2030. Let’s help you understand how AI systems can advance cyber-security while sharing the security risks and best practices necessary to navigate potential challenges. Our guide will also outline the prospects of whether and how a cybersecurity profession remains relevant.
The Impact of AI on Cyber-Security
AI plays an important role in threat detection, data security, and automation today. That’s why many are choosing to learn how to use AI in software development to improve their intelligence using AI and cybersecurity for advanced protection. The artificial intelligence impact on cybersecurity has allowed businesses to improve authentication processes, vulnerability detection, data analysis, and more.
Key Difference Between AI and Cybersecurity
Cyber-security is the focused discipline professionals use to protect sensitive data, unlike computer science, which is a broader discipline that ensures professionals have the expertise to understand every aspect of language and code within computer systems. One of the leading agencies that ensure data security worldwide is the Cybersecurity and infrastructure Security Agency (CISA).
Meanwhile, those with a computer science degree can unlock more opportunities in cybersecurity because they understand every aspect of artificial intelligence technology, languages used in computer systems, and different AI used for data security. Computer science better refers to those who use AI in cybersecurity because they understand computers and computational systems.
Different Kinds of Artificial Intelligence (AI) Explained
Different types of AI mirror human intelligence to support data scientists and cyber-security specialists. AI algorithms can analyze data at speeds humans can’t possible match, and some technology has the advanced capabilities like speech recognition or natural language processing that can perform tasks on behalf of those using AI to improve cybersecurity.
- Generative AI (Gen AI)
Generative AI systems are another technology transforming the cyber-security industry. How can generative AI be used in cybersecurity? Generative AI systems can help security professionals implement threat intelligence or detect new threats using the next best thing to human intelligence.
- Large Language Models (LLMs)
Large language models are a form of AI that can analyze vast amounts of structured and unstructured data from threat intelligence reports, cybersecurity-related text, historical data, networks, and security alerts. Large language models have between 110 million and 340 billion parameters that define them.
- Machine Learning (ML)
Machine learning is another technology that shows how artificial intelligence influences cybersecurity. Machine learning algorithms are capable of preventing zero-day attacks by analyzing various behaviors to predict unusual patterns and identify potential threats. ML models can even prevent future attacks.
Example of AI Technology in Cyber-Security
An example of AI technology being used to detect cyber threats across networks is in the banking sector. AI algorithms don’t merely provide data security benefits to the financial industry. AI and cybersecurity models work closely to improve and automate fraud detection.
The Journal of Accountancy shows how financial experts can deploy AI and cybersecurity measures to automate fraud detection like cyber attackers automate their techniques. Another example is how the healthcare industry ensures data security by automating repetitive tasks in cyber-security.
How AI Systems Improve Cyber-Security
AI technology can help organizations protect every process and bite of information. Cybersecurity AI systems do more than just provide data security. Discover how AI can enhance the security of multiple business operations and processes to protect sensitive information and automate other security tasks.
Real-Time Threat Detection and Incident Response
Morgan Stanley suggests that AI can detect actual problems faster and more efficiently than humans, enhancing threat detection and incident response capabilities within organizations. AI is deployed and responsible for analyzing vast amounts of security data in networks and other computer systems to find patterns that may indicate the potential for cyber threats. AI also recommends real-time responses.
Enhance Access Controls and Authorizations
The rise of AI in access control has impacted multiple industries, including physical and data security. Organizations are using AI cybersecurity systems to enhance biometric authentications for humans interacting with their software systems to reduce attacks and security risks. Other ways AI has advanced authentication protocols is through facial and speech recognition and greater accuracy.
Comprehensive Management of Vulnerabilities
Managing vulnerabilities begins with businesses identifying security risks in a process or systems. KPMG recommends using AI and ML models to detect vulnerabilities in software code to identify risks. These systems provide the insights to help security teams find vulnerable code that would normally be overlooked during a manual inspection, also reducing the risk of human error.
Protect Systems From Malware and Phishing Attacks
McKinsey confirms that the digital world saw a 1200% surge in phishing attacks in 2022 because malicious actors use generative AI to create and process sophisticated and authentic-looking attacks. Organizations can fight fire with fire by using generative AI to conduct malware analysis and identify potential threats to protect any process, email, or file system from phishing attempts.
Behavioral Authentication and Analysis
Experts have leveraged the integration of ABA with artificial intelligence to analyze the behavior of hackers, humans, systems, and even security teams on the internet. Professionals use AI systems with behavior analytics to identify threats of those interacting with networks to find potential attacks before they occur, implementing zero-day prevention strategies with simple automation.
Continuous Learning Through Advanced AI Algorithms
Organizations can prevent future attacks and protect operations by leveraging one of the key factors of how AI influences cybersecurity. Teams use AI to find vulnerabilities and identify threats quickly and efficiently. However, ML models are capable of learning, adapting, and adjusting their approach to cyber-security through continuous learning protocols.
Predictive Analytics for Future Risk Management
Recent research on predictive analytics in cybersecurity shows how it’s possible for experts to forecast and prevent looming concerns with early risk pattern identification. Cyber-security specialists can deploy AI with predictive analytics tools to find patterns that indicate the potential risk of upcoming warnings, taking a proactive stance against the cyber threat detection and incident response.
The Unintended Consequences of AI in Cyber-Security
There have been some unintended consequences that originated from AI and cybersecurity. Various factors could lead to potential damage from hackers using AI to disrupt the cyber-security industry. The challenges can be navigated but help you understand the difference between AI and cybersecurity.
Biased and Deceptive Content
Firstly, AI and machine learning algorithms aren’t immune to biased opinions from the training data provided. However, data poisoning is the greater threat, with malicious characters who have explicitly programmed inaccurate or malicious code into the AI model used in cybersecurity.
Organizations can suffer financial losses when they use AI work for systems or security or to automate specific tasks after the AI algorithms became the target of data poisoning. Hackers use data poisoning to manipulate how AI makes decisions and the incident responses suggested after threat detection.
Greater Threat Sophistication and Volume
Another threat to cybersecurity using AI is when malicious attackers use generative AI to design sophisticated attacks based on historical information and continuous learning models. The National Cyber-Security Centre shows the impact of AI on cyber threats, revealing how techniques have become far more sophisticated and grown in volume. The advanced threats pose cybersecurity challenges.
False Positives and Negatives
AI can hallucinate and alert security specialists to false positives. False positives have long been a concern for the cybersecurity industry, and AI hasn’t made things easier. Cybersecurity professionals reviewing the threat detection alerts will reduce the likelihood of false alerts to ensure proper security based on a close collaboration between AI and human analysts.
Ethical and Privacy Concerns
Ethical and privacy concerns are abundant among cybersecurity professionals using AI to enhance cybersecurity and data security. For example, exposing sensitive data to help models learn doesn’t always align with ethical practices. The ethical use of AI in cybersecurity requires professionals to follow best practices and guidelines that meet the highest standards, even when training AI.
How to Develop Cyber-Security Artificial Intelligence Securely
The responsible use of AI in cybersecurity allows organizations to manage future decisions when using software that learn from training data. Follow these best practices and guidelines to ensure AI continues to play an important role in cybersecurity management:
- Know how to choose a software development company that values ethical standards when developing AI systems from scratch.
- Make sure the company keeps you updated throughout the software development life-cycle so that you can review whether regulations are met.
- Follow all the National Cyber-Security Center guidelines for secure AI system development, including providing multiple information sets to train AI.
- Integrate risk management best practices with the development team to make sure they effectively prioritize the cybersecurity measures.
- Remember why software testing is important, and manage how the development team uses penetration testing because cybersecurity requires it, even if AI doesn’t.
A Career in Cyber-security or Artificial Intelligence (AI)
There are some key differences between choosing a career in cybersecurity or AI, with computer science being the only career choice that may involve all the skills necessary to embrace cybersecurity AI as a focused discipline. The learning curve is higher, by the rewards are worth every step because a computer scientist has the skills and expertise necessary for a career in AI, cybersecurity, or both.
Artificial Intelligence (AI) Skills
Identify which skills you wish to learn to start a career related to artificial intelligence (AI):
- Advanced programming skills
- Big Data expertise
- Spark technologies knowledge
- Algorithms and frameworks
- Probability, linear algebra, and statistical analysis
- Domain expertise
- Business intelligence
- Communication skills
- Problem-solving skills
- Critical thinking capabilities
Cybersecurity Skills
These subjects and skills are required for a career in cybersecurity:
- Basic technical skills
- Risk identification and management
- Logical reasoning
- Troubleshooting
- Basic programming
- Penetration testing
- Data management
- Data analysis
- Cloud management
- Communication skills
Cyber-Security and Artificial Intelligence Conclusion
AI and cybersecurity are two different disciplines that have made a huge impact when combined. Cybersecurity focuses on protecting information while AI has proven to automate some operations, even improve them. AI also has some challenges in cybersecurity, but using the best software development companies in Glasgow may just help businesses overcome those obstacles.
Cyber-Security and Artificial Intelligence FAQs
Will AI replace Cybersecurity Teams?
The AI impact on software development reveals some evidence that AI won’t entirely replace the cybersecurity profession. Cybersecurity and AI together can empower individuals and businesses to enter a new era of security and risk management. AI won’t replace cybersecurity, but specialists who combine both disciplines will thrive in the new era of automated detection and protection.
What career opportunities are available for AI and cybersecurity professionals?
Artificial intelligence and cyber-security are two different disciplines and professions, albeit someone can obtain a computer science degree to unlock both opportunities to use AI systems for automated threat detection and advanced cyber-security. However, here are the opportunities available for those focused on cyber-security or artificial intelligence separately:
Opportunities for Artificial Intelligence Professionals:
- ML engineer
- Data scientist
- Robotics engineer
- NLP engineer
- Software engineer
- BI developer
- AI research engineer
Opportunities for Cybersecurity Professionals:
- Information security analyst
- Cloud consultant
- Identity manager
- Senior security consultant
- Penetration tester
- Chief information officer (CIO)
What are the weaknesses of ML algorithms in cybersecurity?
AI machine learning in cybersecurity has some potential weaknesses due to malicious attackers infusing adversarial information into the AI systems. AI models can only depend on the training information provided, while hackers and malicious attackers inject poisonous information to manipulate AI decisions. Continuous learning AI models have a chance of recovery, but most AI is weak to poisoning.
