Artificial intelligence (AI) is a commonly misused term leveraged to sell everything from smart gadgets to new car features. In their purest applications, AI and machine learning (ML) are technologies that have played critical roles in advancing fields such as medicine, manufacturing and marketing.
One sector particularly benefiting from AI is cybersecurity. A study by Cybint revealed 95% of cybersecurity breaches can be attributed to human error. With 88% of organizations experiencing phishing attacks in 2019, securing vital company networks has never been more crucial. AI is increasingly leveraged as the most suitable tool for reducing cybercrime risks.
What are Some Basic AI Functions?
AI encompasses technologies that understand, learn and act based on readily available and newly acquired data. AI replicates several facets of human intelligence by intaking structured training information, analyzing this data for patterns and using this process to craft predictions.
AI programming entails three main processes:
- Learning – This step comprises collecting information and developing rules for transforming this data into actions that programs can follow.
- Reasoning – This process focuses on selecting the appropriate algorithm for every desired action.
- Self-correction – This aspect centers on refining algorithms to intuitively improve performance.
The three main applications of AI are:
- Assisted Intelligence – A simple form of AI used to assist with tasks and processes by leveraging data collections.
- Augmented Intelligence – A slightly more complex AI used to assist and enhance human intelligence, instead of replacing it. Unlike assisted intelligence, augmented intelligence can synthesize existing information with newly-gathered data to craft new solutions.
- Autonomous Intelligence – The most advanced AI form that allows systems, computers and bots to act independently and without human intervention.
How Does AI Differ From Data Analytics?
Since AI has already transformed industries across the globe, companies everywhere have naturally hopped on the band wagon. Although some brands market their applications as AI tools, what they most often offer are programs useful for analyzing data. Although AI and data analytics work hand-in-hand, they remain distinct computer science fields. As the term ‘AI’ is used more and more in advertising, marketing and public relations, it’s vital to understand the difference between AI and data analytics (DA).
Data analytics entails deriving massive information spectra and drawing insights and predictive theories based on data. Data mining yields vast swaths of information that allow business leaders and marketers to better target desired audiences. These data collections can also inform predictive analytics, theoretical scenarios carefully constructed from information derived from past events. Overall, analyzing data is a static process and does not include self-learning.
AI programs, however, become more intelligent as they assess data, learning from experience and becoming better as they go. In some ways, AI and ML implement predictive analytics concepts, except they continually self-evaluate and adjust models without human intervention. AI and ML are adaptive and iterative.
How is AI Integrated with Cybersecurity?
Due to AI’s adaptive quality, it is well-suited to tackle the world’s ever-changing security challenges. With AI, companies storing sensitive data can implement automated threat detection that can keep up with cyber criminals. The role of AI in cyber security is to secure company assets and protect user data.
Several factors position AI as suitable for integration with cybersecurity systems:
- Continual Learning – AI uses machine and deep learning to understand network behavior and cluster identifiable patterns.
- Handling Data – Especially with larger businesses, vast amounts of data are transferred and stored on a daily basis. Human evaluation and securing of this information are both tedious and daunting, however, AI can automatically peruse large data collections for potential threats.
- Eliminating Tedious Tasks – Although cyber criminals change tactics, many attacks remain consistent. AI can easily maintain continual security procedures so that expert cybersecurity professionals may focus on implementing innovative solutions to the company’s most pressing challenges.
Integrating AI into cybersecurity systems results in a number of improved outcomes, including:
- IT Asset Inventory – AI provides organizations with consistently updated information about hardware and software users, behaviors and performances. This monitoring can inform better security practices and help organizations identify vulnerabilities.
- Effectiveness Control – This technology can automatically assess security effectiveness and analyze opportunities for improvement in company networks.
- Explanation – AI can be programmed not just to assess systems and make them more efficient, but also provide explanations for important company stakeholders. This factor is vital for acquiring buy-in from organizational leaders and end users alike.
How Does AI Help Cybersecurity?
As digital cybersecurity applications have been further integrated into systems used by organizations, the vast amount of resulting data generated increasingly demands robust structuring. According to Drexel Associate Teaching Professor of Information Science Thomas Heverin, PhD, “One of the key problems in cybersecurity includes the amount of data that cybersecurity professionals must process in order to make critical cybersecurity decisions.”
As mentioned before, AI employs numerous processes to assist with cybersecurity. The advantages of integrating AI with cybersecurity include:
- New Threat Detection – Although some hacking strategies remain consistent, cyber criminals are constantly conceiving new methods for infiltrating sensitive systems. By monitoring behavior and identifying user patterns, AI identifies unusual activity and alerts security teams.
- Bot Blocking – A large section of online traffic is composed of bots, many of which can pose threats to company systems. AI programs can understand organic traffic patterns and discern allowable bots, like search engine crawlers, from malicious ones. This process can also assist company leaders when assessing user journeys and webpage activity.
- Breach Prediction – Since AI programs catalogue IT inventory, they can monitor particular hardware and software types and identify vulnerabilities, helping security teams predict potential breaches. Many AI applications can also provide prescriptive information to solve IT security challenges.
- Endpoint Protection – As remote work becomes more common, end point protection has become vital for any organization seeking to protect company assets from a distance. As opposed to antivirus software that functions on signatures, AI programs identify behavior patterns, geolocation and time zones to detect suspicious activity.
What Organizations Leverage AI for Cybersecurity?
Due to AI’s enormous potential to render company systems more secure, several reputable organizations have already integrated AI into their cybersecurity:
- Google – Gmail was particularly early to the AI cybersecurity game. Google leverages deep learning to improve security and adapt to technology changes.
- IBM – This company’s much-heralded technology, Watson, is especially effective at consolidating knowledge and identify security threats.
- Juniper Networks – This organization is focused on innovating autonomous AI to provide more secure and user-friendly networks.
As AI effectively automates tedious tasks, learns and adapts to new cyber crime tactics, and improves remote worker security, more and more organizations are integrating AI programs into their digital security protocols.
What Threats Does AI Pose to Cybersecurity?
As AI is leveraged to thwart possible threats, this technology is conversely used by bad actors to breach systems. Some examples of malicious AI use include:
- Data Poisoning – This tactic entails altering AI data to trick program models into forecasting incorrect predictions. An example of data poisoning is when attackers input algorithms into a machine learning model to identify malicious data as benign.
- Generative Adversarial Networks (GANs) – This strategy entails crafting a mirror AI system to mimic normal traffic behavior, distract from damaging attacks and extract sensitive data.
- Manipulating Algorithms – Since AI relies on algorithms, if cyber criminals can understand these models, attackers can manipulate AI to execute incorrect actions. One shining example of this type of breach is in the cryptocurrency sector. Hackers have been able to identify trading algorithms and alter them.
It’s important to note that apart from threats AI may present to cybersecurity, this technology is also limited. Heverin says, “Even though AI can be used to model the complexity of the real-world, the outputs need to be evaluated by humans to determine how well AI models represent reality and how those models need to be tweaked. AI models can be used to aid cybersecurity professionals in making decisions and judgements; however, AI models will never be able to make all cybersecurity decisions and judgments.”
AI is Making Systems Smarter
There is no question AI is making cybersecurity systems smarter. Whether this technology is used for securing authentication, threat detection or bot battling, AI and ML can prevent bad actors from infiltrating and manipulating company networks.