AI is integral to modern society, from businesses using the tech to sort and manage databases to corporations outfitting their systems with cybersecurity protections. AI’s role in cybersecurity is often reactionary, offering enhanced threat detection, immediate event response, and significant advantages for corporations that utilize it – while deterring and defending against cybercriminals.
How we use AI determines its usefulness – and concerning cybersecurity, AI has a variety of considerations. Find the most significant highlights of AI in cybersecurity below, including its potential advantages and risks.
AI-powered Cybersecurity Solutions
AI has worked alongside cybersecurity for decades. All its variations offer something different and integral to data safety, from automated direct threat responses to machine learning.
There are various types of AI globally, most with specific jobs – like sorting your emails or parsing data for building reports – but there are also those with more challenging roles, like conversationalists, trained to think and speak like a human counterpart. Each variation of AI has unique advantages and risks for those utilizing it, and all are necessary to consider.
- Narrow AI: This category encompasses all AI, from niche government tech to voice assistance and self-driving vehicles.
- Machine Learning: This AI is most commonly associated with having the ability to “learn” from data sets and derive decisions without explicit programming.
- Deep Learning: AI of this category is modeled after the human brain, and is often used for completing advanced tasks that require multiple elements for conclusion.
- Generative AI: The internet’s most beloved variation of AI, this tech allows for inputting data like video media and outputs unique content from the source material.
How AI Responds to Cyber Threats?
Cybersecurity and AI work hand-in-hand to provide solutions for online threats. AI is particularly advantageous in this environment because it detects and responds to cyber threats faster than traditional methods. Where it once took days to respond to a cyber threat, immediate AI responses can completely stop a threat before it can cause damage. It also allows for additional insights into security events. Moreover, AI-powered cybersecurity solutions offer various benefits depending on the model.
AI can suggest vulnerability management before a threat breaks through a business’ defenses, including scanning networks and internal systems for vulnerabilities or simulating attacks to gather potential impact reports. Other AI offers behavioral analytics, which is highly valuable for organizations monitoring user behaviors for threat detection. These AI models analyze regular user and network traffic, alerting defenders to suspicious threats like increased or spiked page sessions or abnormal purchases.
Machine Learning in Threat Detection
Machine learning AI can access and “learn” from data sets given to it; subsequently, some models can react to threats without the AI being explicitly commanded to react in a particular way. These reactions may vary from making system status suggestions – like notifying experts that there is a vulnerability somewhere – to actively stopping threats within an environment, sometimes by isolating broken systems and other times by blocking suspicious attachments and IP addresses.
Machine learning also offers unique predictive solutions and incident forensics. For example, AI can assess and “learn” from user activity. It can analyze behavior and compare it to other elements within a system; this can be highly beneficial for reviewing a threat’s sequence of events, which consequentially informs cyber experts on system weaknesses, user errors, and necessary improvements.
The Rise of AI-driven Threats
Cyber threats are an increasing danger in this day and age. AI offers these criminals new avenues to orchestrate plots on consumers and employees, identify and exploit system vulnerabilities, and launch various attacks to achieve their ends.
How Cybercriminals Leverage AI?
AI is beneficial for cybercriminals in many ways. Depending on their goals, criminals can gather insights into their targets – as in social engineering attacks – and create algorithms that brute force a target’s systems directly or collect information for an attack later. However, their approaches differ depending on their technology and available resources. The most significant criminal threats concerning consumers are AI-driven phishing and deepfake content.
Phishing is so-called due to its relation to traditional fishing; a criminal uses information about a target to “lure” them into sharing particular data, typically account or financial details. Criminals can also target victims with generative content like deepfakes. Such content can include videos, voices, text, music, and any other form of media, all stemming from a single source. Government officials, corporate email accounts, and instructional directives have exceptionally high chances of doppelgangers, which trick consumers and employees into sharing sensitive information.
AI Vulnerabilities and Exploits
For all of AI’s benefits to security, it also hosts unique vulnerabilities that everyone must consider before adopting the tech. These vulnerabilities are often compounded by criminal events, like injections, where malicious content is released into a system to find weaknesses; or when automated AI gathers fake or manipulated data as part of an authentic analysis, thus resulting in criminal data being mistaken for authentic data.
Examples of AI Attacks and their Consequences
AI cyber attacks vary as much as their consequences. Further, AI-driven threats consistently evolve, forcing cyber defenders to develop more advanced securities or risk significant financial, reputational, and data-based losses. AI attacks differ by industry and corporation, but some of the most common include:
- Automated Malware: AI today is already great at creating complex algorithms, but add in the malicious deeds of a cybercriminal, and malicious code can be written in a few minutes. Sent to a target without the ability to detect this code, and a victim’s systems can be overrun with aggressive opportunists.
- Data Poisoning: AI depends on “clean” data sets for training purposes; consequently, if a criminal can inject “dirty” or “poisoned” data into these authentic sets, they can influence how the AI will react to a particular element in their environment. These attacks are prominent in the healthcare, automotive, and housing industries.
- Physical Manipulation: Self-driving cars require AI to get passengers from one place to another – but if a cybercriminal is involved, there’s no guarantee that the traveler will make it to their destination. Construction equipment is equally vulnerable to these schemes, risking worker and consumer lives on a work site.
- Impersonations: As mentioned above, deepfakes are an increasingly worrying threat today, with business emails and government officials being convincingly “copied” and puppeteered into saying and doing whatever a criminal wants; however, these aren’t the only people targeted by impersonations – some criminals arrange fictional kidnappings to trick victim’s families into paying ransoms, while others create fake donation websites for seemingly good causes.
Challenges in AI Cybersecurity
AI is excellent at many things, but there remain challenges to its implementation. Improper training, zero-day vulnerabilities, costs, falsities in alerts, and ethical concerns like potential discrimination and lack of transparency are all significant issues with AI in cybersecurity.
The Limitations of AI in Cybersecurity
In 2023, a zero-day vulnerability in file management software called MOVEit exposed the sensitive information of thousands of organizations and millions of people. The vulnerability was previously unknown, but once cybercriminals had discovered it, they spent no time stealing organizations’ data globally. Such are the dangers of zero-day vulnerabilities, and AI defenses are limited in detecting such weaknesses.
AI is also limited in cybersecurity practice because it can be expensive. AI solutions require niche hardware, unique infrastructure compatible with legacy systems, and significant processing resources, which must be powered, maintained, and updated regularly.
False Positives and False Negatives in AI Security
Falsities are also a challenge in AI cybersecurity; a false positive, which indicates suspicious activity where there is none, can waste resources and cost thousands in investigative work. Conversely, a false negative is even more destructive, with malicious activity being incorrectly labeled as authentic – granting criminals the “okay” to move within a network or database freely. How cybersecurity experts limit these falsities differs between industries and technology, but it is essential to consider.
Ethical Concerns of AI Decision-making
Another challenge in AI cybersecurity is its ethical obligations. An improperly trained AI can create significant issues in industries with unfair targeting and discrimination biases, as is being discovered in healthcare networks utilizing care decisions via AI suggestions.
There are also privacy risks associated with AI; the rapid development of the technology necessarily results in legislation struggling to keep up, which can result in a lack of transparency, as well as limited regulation and guidelines – that can scare a consumer into the hands of a competitor who offers more clear ethical values.
Are AI Tools Enough to Protect Data?
Even the most advanced AI isn’t enough to comprehensively protect data. The technology requires constant monitoring and updates, and it is alongside human expertise that defenders can best utilize AI to protect sensitive data.
AI Tools are Not Enough for Comprehensive Cybersecurity
Despite its significant benefits, AI is still insufficient for comprehensive cybersecurity. AI tools alone can offer many interesting and valuable insights into a system’s data, but they cannot react to all threats in the way that data owners might want.
Even AI compatible with machine learning elements would struggle to respond to security events like zero-day vulnerabilities – precisely because the AI wouldn’t have “seen” or “experienced” the security issue before. As a result, the best way to protect consumer and organization data is by implementing AI solutions and human expertise.
Combining AI Solutions with Human Expertise
Advanced AI protections can be a game changer for organizations with the resources and funds to operate them – but for those who don’t have these sorts of options, AI can still be significantly beneficial. For example, AI can compile and analyze account behaviors. While it may not flag an account as suspicious, human experts can be trained to detect and react to strange phenomena within the compiled data.
Continuous Monitoring and Updates for AI Security
AI also requires continuous monitoring and updates; subsequently, unless a business can fund the production of defenses consistently, it may be left vulnerable to advanced cyber threats. Moreover, predictive analytics and proactive defenses can become ineffective the longer the technology runs without necessary updates – potentially generating incomplete or incorrect insights, putting organizations and consumers at risk for cyber abuse.
Looking Ahead: The Future of AI in Cybersecurity
The future of cybersecurity is evolving, with cyber defenders implementing new and advanced tech to ensnare cybercriminals. However, these developments aren’t enough to protect data by themselves – businesses and individuals will still need to utilize best practices to protect themselves from online threats.
Emerging AI Cybersecurity Trends
The technology of a cybercriminal is constantly updating and changing. As a result, widely used cyber defenses can be a step behind these new developments. However, new AI tech approaches these issues with various solutions, including tools like “deception tech”, where networks create decoy systems and honey pots for criminals to “fall” into, or “zero trust architecture”, where the system must verify identities at every level, eliminating the freedom that a criminal would otherwise have in a victim’s network.
AI and the Future of Digital Security
Despite AI’s current challenges, AI will be necessary to consider for the future of cybersecurity. The tech will become a vital part of protecting the data of consumers, businesses, governments, and industries across the globe. Moreover, criminals will aggressively target small businesses, which have long been thought not to be a target of cyber threat activity, primarily because they have little to no cybersecurity funds or resources to protect their systems.
Best Practices for Businesses and Individuals to Stay Secure
Large corporations, small businesses, and individuals are all targets for cyber threats in our increasingly AI-driven world. It can be a scary thought, especially with events like 2024’s Mother of All Breaches (MOAB) attack, which saw over 26 billion consumer records exposed on the dark web. However, even in times of significant data risk, there are still practices that businesses and individuals can use to keep themselves secure.
- Utilize staff training to inform employees of the threats they may face while at work; teaching them to recognize signs of a cyber threat can go far in stopping one.
- Strengthen level access options, which require verification procedures before granting access; this will stop a threat in its tracks and can isolate infected systems.
- Assess and mitigate insider threats, such as disgruntled employees or the unintentional sharing of sensitive data over social media; phishing threats often target those who share corporate details on public posts and accounts.
- Begin introducing a transition into AI tech early on, especially in environments where employees will likely resist its use. Introducing the technology into a workforce can help build employee confidence in its abilities while allowing initial insights into a system environment without the commitment of substantial costs.
AI is central for businesses and individuals looking to evolve with our ever-changing world. More and more, those in charge of cyber defenses will look to utilize AI for its potential – but in its current state, corporations will need more than a good AI solution to protect the sensitive data of their systems and consumers.
These security experts must utilize human training and expertise, along with AI technology, to best serve their corporations and client base; without both, they leave themselves open to cybercriminals looking to abuse their vulnerabilities.
