With so many roadblocks and obstacles to overcome in today’s cyber landscape, organizations face more threats than ever before. Unfortunately, new problems often emerge before the old ones have even been solved. Amidst this ever-evolving threat landscape, we’ve compiled a list of the top 5 emerging cyber security challenges.
The Current State of Cyber Security
Modern IT is rife with danger. Regardless of an organization’s activities, sector, or size, the value of data and influence over digital capabilities cannot be understated. As a result, cybercriminals continually experiment with new techniques to compromise cyber security, data, and technological processes. To defend against these developing methods, cyber security professionals must adapt to all current and emerging threats.
Today’s top 5 emerging cyber security challenges comprise:
- Cloud computing vulnerabilities
- AI-enhanced cyberthreats
- Machine learning obstacles
- Smart contract hacking
- Fake or fraudulent content
Investigating the Top 5 Emerging Cyber Security Challenges
Our increasing reliance on IT is a double-edged sword, and—in equal measure—cyber security becomes increasingly critical to protect organizations and ourselves from digital wounds carrying material consequences. While there’s no doubt that many of the IT innovations covered below have increased data-driven capabilities and made our lives more comfortable than ever before, the creation of new vulnerabilities and their exploitations requires up-to-date threat intelligence and cyberdefense techniques.
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The cloud is beneficial to organizations and consumers alike. It makes online tasks like sharing and collaboration much easier while streamlining online purchases, customer support, and more. For organizations, in particular, cloud computing enables advanced service delivery and processing without demanding increased hardware capabilities and on-premise storage.
However, despite its usefulness, some emerging challenges are unique to the niche of cloud computing.
Unsecure Cloud Usage
One of the cloud’s primary uses revolves around data storage. Accordingly, cloud storage environments are targeted by hackers and other malicious actors. An overeagerness to adopt cloud capabilities without adequate protections has led to 40% of global organizations suffering a data breach since October 2020.
These leaks can be catastrophic, depending on the size and scope of the breach, so it’s critical to ensure that all of your cloud-based storage systems are properly configured, patched, and updated. Regarding cloud security, you organization should establish and enforce baseline policies that help ensure:
- According to a “zero trust” security model, all users are continually authenticated, regardless of whether their network connection occurs internally or externally to your organization.
- To provide additional protection, all sensitive data is encrypted—both “at rest” and in transit.
Without implementing cloud security architecture and measures, your organization remains exposed to cyberthreats.
Unsecure Cloud Service Integrations
Storage isn’t the only cloud vulnerability. The surge in cloud service integrations challenges traditional notions of the cyber security perimeter. Effectively, no perimeter exists, as third-party connections provide additional network entry point vulnerabilities.
Insecure or misconfigured APIs also present problems that are unique to the cloud. Up to two-thirds of cloud-related breaches are traceable back to API misconfigurations. Therefore, it’s a significant issue that has the potential to affect nearly every cloud-using organization and individual user today.
Just like unsecure storage, these issues are easily exploitable by determined hackers and other malicious actors.
The danger of social engineering attacks continues to grow, thanks in part to such widespread cloud implementation.
Phishing is the most common form of social engineering seen on the web and in the cloud. A basic phishing attempt unfolds when a hacker sends a fraudulent email or other communication while intending to gain the potential victim’s trust. This often requires them to impersonate an executive-level professional, government agency, or other authority and foster trust, fear, or distress in the recipient.
Hackers generally utilize social engineering strategies to trick the victim into disclosing account credentials or sensitive data (e.g., financial). Phishing attempts targeting cloud access likely seek the former.
Phishing usually comes in one of two forms:
- Voluntary – The hacker tricks their victim into revealing sensitive information willingly through seemingly legitimate pretenses. For example, a hacker impersonating a bank’s customer support representative might try to trick their victim into signing up for additional services that don’t actually exist (i.e., “baiting”). Once they have their information, the hacker can open up new accounts or access those that already exist.
- Verification – In these social engineering attacks, the hacker tries to trick their victim into verifying information. For example, the hacker might impersonate a third-party cloud service your organization integrates with and ask their victim to verify their login details. Once provided, the hacker has access to the cloud environment and any other sites that use the same login credentials.
One of the most significant threats to cloud security is that many people reuse the same passwords across numerous accounts—professional and personal. Therefore, even if one of your employees falls victim to a social engineering scam targeting them as an individual, your cyber security may still become compromised.
Next-gen artificial intelligence (AI) is revolutionizing how we live our daily lives. It’s an exciting field with tons of potential for the future, from smart home appliances that control themselves to deeper business insight through AI-powered analytics.
But AI can also be used for malicious purposes. Moreover, these AI-enhanced cyberthreats are often unmanageable with basic-level cybersecurity software due to their highly advanced nature. Instead, most of them require a professional and customized approach.
The emergence of next-gen AI has seen some of these same advanced concepts also applied to recent phishing attacks. Since traditional phishing methods have remained a substantial threat for years, tech-savvy users are starting to recognize the common signs associated with phishing.
However, this AI-enhanced cyberthreat is more sophisticated than ever before. Since the latest AI systems can now better mimic human speech and conversation patterns and adapt according to inputs, they pose a much more significant threat than older, outdated phishing tactics.
Much like phishing, threats like malware and ransomware have been around for years. However, the latest iterations utilize next-gen AI for a more calculated and effective approach.
Instead of tricking the user into running an infected program, AI-driven malware and ransomware scan a potential victim’s device to mimic or take over its normal system operations. These malicious programs can be executed when users unlock their phones or boot up their laptops.
Bypassing Reliable Security Measures
One of the earliest examples of an AI-enhanced cyberattack happened in 2010 when ticket scalpers used custom scripts to automatically solve Ticketmaster’s captchas and bypass configured purchasing limitations controlling the limit of tickets per customer. The attack made international headlines because, until then, captchas were the online standard for separating humans from machines.
The sophistication and capability of AI technology have only increased since. With current AI tech capable of mimicking expected human behavior on targeted networks, the possibilities are left up to the hacker’s imaginations.
AI fuzzing is a threat before, during, and after machine training. While it is often used in vulnerability scanning and assessment by cybersecurity professionals, it also has nefarious applications.
Since it’s a powerful technology designed to highlight vulnerabilities within a computer’s network, hackers leverage the tool to discover new exploits. Zero-day vulnerabilities, referring to unknown vulnerabilities and known vulnerabilities without a developed or deployed patch, pose particularly heightened threats.
A subdiscipline of AI, the concept of machine learning is still relatively new. When utilized for its original purpose, machine learning facilitates advanced data analytics by identifying trends, recognizing patterns, and separating useless information from valuable, actionable data.
As a result, machine-learning-enhanced cyberthreats often focus on attacking data integrity. This specific threat can be split into two distinct categories:
- Before or during machine training – Modern computer systems aren’t capable of learning independently. Instead, they require training and human inputs to recognize patterns and apply new concepts. AI-enhanced cyberthreats that exploit incomplete training fall into this category.
- After machine training – An intelligent computer system can only be put into commission after it’s been trained. However, training errors or oversights can be exploited once machine learning capabilities have been launched. Threats that affect a system after its initial training period are classified here.
Before or During Machine Training
Data poisoning is one of the biggest threats faced before or during machine training. Also known as “machine learning poisoning,” this occurs when training is intentionally based on incorrect or manipulated data. Instead of providing the system with correct solutions and guidance, the hacker intentionally misdirects the system.
Machine learning poisoning undermines data integrity. If the training data comes into question at any time, the entire training program requires personnel to perform remediation—if it’s salvageable. Instead, algorithms may require recoding from scratch, or the entire development program might have to be scrapped.
After Machine Training
Some cyberattacks take place after an intelligent machine has been trained. Some of the most common attacks include:
- Adversarial or evasion attacks – These AI-enhanced cyberattacks trick the trained machine into forecasting inaccurate information.
- Transfer learning attacks – Pre-trained systems are sometimes used to expedite deployment. Still, once live, these can be hijacked by hackers who replace the stock definitions and algorithms with malicious code.
- Output attacks – If hackers can access a system’s output before others, they can easily change the results to meet their nefarious intentions.
Because machine learning is still in its infancy, IT experts have plenty of time to optimize and refine the technology to mitigate these issues. We can expect to see future machine learning systems address problems like this as the technology evolves. However, until then, these threats require substantial cyber security considerations and implementing measures to prevent cyberattacker access.
Made possible due to blockchain technology, smart contracts are a digital replacement to traditional, hardcopy contracts. The finance industry has already benefited immensely from the implementation of smart contracts, and other sectors—such as healthcare, medical research, and even administration—are all exploring potential applications of digital smart contracts.
Unfortunately, smart contract hacking has already reared its ugly head. A motivated hacker recently stole $31 million by exploiting a known bug in smart contract software, and more incidents are sure to come.
An earlier case involving the Decentralized Autonomous Organization, or DAO, saw the organization using smart contracts to handle funding on behalf of various cryptocurrency-backed projects. Unfortunately, an ambitious hacker exploited a discovered vulnerability to steal $3.6 million in cryptocurrency, which ultimately caused the digital coin provider to split into two separate entities.
Smart contracts have numerous advantages over traditional contracts. They’re quicker, easier, and more affordable. However, given some of the recent concerns over smart contract hacking, some are wondering if they’re even worth the extra trouble.
Fake or Fraudulent Content
Often referred to as “deepfakes,” fake or fraudulent content runs rampant on many online platforms and in various communities. According to some researchers, this disturbing new trend is the most substantial crime made possible by AI to date.
Most social media users are familiar with the idea of deepfake content. Similar to the concept of fake news, this type of content takes it one step further by interjecting a false story with misleading digital content such as photos, videos, and audio to further its apparent legitimacy. Some of the earliest examples of deepfake content are quite convincing.
Once created, this digital likeness can be used for nearly any purpose. They can be inserted into videos or images—including advertisements, promotions, or, in the most damaging cases, surveillance footage and crime scene imagery—and used at the hacker’s whims.
It’s easy to understand the potential repercussions of such technology. While it’s been mostly celebrities who have been the most vocal about deepfakes up until now, everyone has a reason for concern.
Bolstering Cyber Security To Overcome Emerging Threats
Organizations operating online can’t afford to ignore current and emerging cyber security concerns. The sophistication of these cyberattacks requires providing all users with up-to-date threat intelligence to recognize suspicious activity more easily. As new threats emerge, they must be accounted for during security awareness training and program implementation.
For more information on today’s top 5 emerging cyber security challenges and other digital threats, contact RSI Security today.