Artificial Intelligence, or AI, has been around for decades, but only in recent years have we seen a massive surge in its development and application.
The advent of advanced algorithms, Big Data, and the exponential increase in computing power has propelled AI‘s transition from theory to real-world apps.
However, AI has also unveiled a darker side, attracting cyber attackers to weaponize the technology and create havoc in ways unimaginable!
Deloitte states that 34.5% of organizations experienced targeted attacks on their accounting and financial data in 12 months. This shines a light on the importance of maintaining a risk register for tracking potential threats.
Another research further emphasizes this – a staggering 80% of cybersecurity decision-makers acknowledge the need for advanced cybersecurity defenses to combat offensive AI. Let us dive deep into the double-edged nature of the technology.
Top 4 AI-enabled phishing and cybersecurity threats to know
Cyber threats are on the rise, both in terms of complexity and volume. Here are four examples that are creating a buzz in today’s security landscape for all the wrong reasons:
1. Deepfakes
This manipulative technique creates realistic-looking and highly convincing video, audio, and image content that impersonates individuals and organizations using AI algorithms.
Deepfakes can push fake news or negative propaganda to confuse or skew public opinion and imitate the victim’s voice or appearance to gain unauthorized access to secure systems.
Using this technology, cyber attackers can instruct employees to perform actions that compromise the organization’s security, such as sharing confidential data or transferring funds.
Remember when in 2019, the CEO of a UK-based energy firm got scammed into wiring 220,000 to a scammer’s bank account because he thought he was speaking to his boss on the phone, who had the recognizable “subtle German accent?”
The voice, in fact, belonged to a fraudster who used AI voice technology to spoof the German chief executive. Deepfakes are known to make phishing attempts much more personable and believable!
2. Data poisoning
While data poisoning is typically associated with Machine Learning (ML), it can also be applied in the context of phishing.
It is a type of attack where misleading or incorrect information is intentionally inserted into a dataset to maneuver the dataset and minimize the accuracy of a model or system.
For example, most people know how prominent social media companies like Meta and Snap handle data. Yet, they willingly share personal info and photos on the platforms.
A data poisoning attack can be launched on these platforms by slowly corrupting data integrity within a system. Once the data gets tainted, it leads to several negative consequences, such as:
- Inaccurate predictions or assumptions
- Disruptions in day-to-day operations
- Manipulation of public opinion
- Biased decision-making
Ultimately, data poisoning is considered a catalyst for financial fraud, reputation damage, and identity threat.
3. Social engineering
It typically involves some form of psychological manipulation, fooling otherwise unsuspecting individuals into handing over confidential or sensitive information that may be used for fraudulent purposes.
Phishing is the most common type of social engineering attack. By leveraging ML algorithms, cyber attackers analyze volumes of data and craft convincing messages that bypass conventional cyber security measures.
These messages may appear to come from trusted sources, such as reputable organizations and banks. For example, you might have come across an SMS or email like:
- Congrats! You have a $500 Walmart gift card. Go to “http://bit.ly/45678” to claim it now.
- Your account has been temporarily locked. Please log in at “http://goo.gl/45678” to secure your account asap!
- Netflix is sending you a refund of $56.78. Please reply with your bank account and routing number to receive your money.
Cyber attackers want to evoke emotions like curiosity, urgency, or fear in such scenarios. They hope you would act impulsively without considering the risks, potentially leading to unauthorized access to critical data.
4. Malware-driven generative AI
The powerful capabilities of ChatGPT are now being used against enterprise systems, with the AI chatbot generating URLs, references, functions, and code libraries that do not exist.
Through this, cyber attackers can request a package to solve a specific coding problem only to receive multiple recommendations from the tool that may not even be published in legitimate repositories.
Replacing such non-existent packages with malicious ones could deceive future ChatGPT users into using faulty recommendations and downloading malware onto their systems.
How to protect your organization against AI phishing scams
As the sophistication levels of cyber attacks continue to evolve, it is essential to adopt several security measures to keep hackers at bay, including:
1. Implement the Multi-Factor Authentication (MFA) protocol
As the name suggests, MFA is a multi-step account login process that requires additional info input than just a password. For instance, users might be asked to enter the code sent on their mobile, scan a fingerprint, or answer a secret question along with the password.
MFA adds an extra layer of security and reduces the chances of unauthorized access if credentials get compromised in a phishing attack.
2. Deploy advanced threat detection systems
These systems use ML algorithms to analyze patterns, identify anomalies, and proactively notify users about potentially dangerous behaviors such as deepfakes or adversarial activities, thereby giving organizations a leg up over cybercriminals and other threat actors.
Many Security Operational Centers use Security Information and Event Management (SIEM) technology in tandem with AI and ML capabilities to enhance threat detection and notification.
The arrangement allows the IT teams to focus more on taking strategic actions than firefighting; it improves efficiency and cuts down the threat response time.
3. Establish Zero Trust architectures
Unlike traditional network security protocols focusing on keeping cyber attacks outside the network, Zero Trust has a different agenda. Instead, it follows strict ID verification guidelines for every user and device attempting to access organizational data.
It ensures that whenever a network gets compromised, it challenges all users and devices to prove that they are not the ones behind it. Zero Trust also limits access from inside a network.
For instance, if a cyber attacker has gained entry into a user’s account, they cannot move within the network’s apps. In a nutshell, embracing Zero Trust architectures and integrating them with a risk management register helps create a more secure environment.
4. Regularly update security software
This measure is commonly overlooked, and it is essential for maintaining a strong defense against AI-driven phishing and cyber security threats. Software updates include patches that address known anomalies and vulnerabilities, ensuring your systems are safe and secure.
5. Educate and train your employees
Training programs come in handy to raise awareness about the tactics employed by cyber attackers. You must, therefore, have the budget for teaching your employees different ways to identify various phishing attempts and best practices for responding to them.
Over to you
The role of AI in phishing indeed represents a frightening challenge in this day and age. Addressing such cybersecurity threats requires a multi-faceted approach, including user education, advanced detection systems, awareness programs, and responsible data usage practices.
Employing a systematic risk register project management approach can help you enhance your chances of safeguarding sensitive data and brand reputation. In addition, you should work closely with security vendors, industry groups, and government agencies to stay abreast of the latest threats and their remediation.
The post Digital Deception: Combating The New Wave Of AI-Enabled Phishing And Cyber Threats appeared first on Datafloq.
