As of my last knowledge update in December 2023, several emerging cybersecurity threats were gaining attention and could continue to be significant in 2024: Ransomware Attacks: Ransomware continues to be a significant threat, with attackers evolving their tactics and targeting not only large organizRead more
As of my last knowledge update in December 2023, several emerging cybersecurity threats were gaining attention and could continue to be significant in 2024:
-
Ransomware Attacks: Ransomware continues to be a significant threat, with attackers evolving their tactics and targeting not only large organizations but also small and medium-sized businesses, local governments, and critical infrastructure.
-
Supply Chain Attacks: As seen in recent years, supply chain attacks pose a serious threat to organizations, especially those with extensive third-party dependencies. Threat actors are increasingly targeting software vendors, managed service providers, and cloud service providers to infiltrate their customers’ networks.
-
Zero-Day Exploits and Vulnerabilities: The discovery and exploitation of zero-day vulnerabilities remain a significant concern. Threat actors can use these exploits to launch targeted attacks, and these vulnerabilities pose challenges for organizations and security teams to patch and mitigate.
-
Deepfake Technology: The use of deepfake technology for social engineering attacks and misinformation campaigns is a growing concern. This technology can create convincing fake audio and video content, which can be used to deceive individuals and manipulate public opinion.
-
Internet of Things (IoT) Vulnerabilities: With the proliferation of IoT devices in homes, businesses, and industrial settings, the vulnerabilities associated with these devices present a significant cybersecurity threat. Compromised IoT devices can be used for large-scale botnet attacks and data exfiltration.
-
5G Security Challenges: The rollout of 5G technology brings increased connectivity and speed, but it also introduces new security challenges. Threat actors could exploit vulnerabilities in 5G networks to launch attacks and compromise critical infrastructure.
-
Artificial Intelligence (AI) and Machine Learning (ML) Threats: While AI and ML technologies can enhance cybersecurity, they also introduce new attack vectors. Threat actors can use AI to automate cyber attacks, create more sophisticated phishing campaigns, and evade detection.
-
Cybersecurity Skills Shortage: The shortage of skilled cybersecurity professionals remains a significant challenge. Organizations struggle to fill critical cybersecurity roles, leaving them vulnerable to attacks and lacking the necessary expertise to secure their environments adequately.
Okay see, AI can be a tool for security but it’s not invincible so fully relying on AI for security can be risky. Now, let’s understand the risks associated with using AI in cybersecurity with an example – Imagine AI as a digital guard, protecting your digital fortress, it identifies and defends youRead more
Okay see, AI can be a tool for security but it’s not invincible so fully relying on AI for security can be risky.
Now, let’s understand the risks associated with using AI in cybersecurity with an example – Imagine AI as a digital guard, protecting your digital fortress, it identifies and defends your system from any cyberattacks.
But even AI has its weakness just like a human guard. The AI systems can be tricked by Hackers who might find loopholes in the AI’s defenses, or even teach it to act in harmful ways.
These are the following risks associated with AI-
1. Adversarial Attacks
AI can be tricked. An attack called AI poisoning where hackers can trick AI systems into making mistakes. Other than that with Evasion Attacks they find sneaky ways to bypass its defenses. And the most common of them is Deepfakes where AI is used to create fake content which seems real leading to reputation damage.
2. Errors and Bias
See, sometimes, AI systems make errors, that is called AI Hallucinations where it can mistake a harmless file for a virus and vice versa. This can lead to false alarms or missed threats.
AI can be biased. If the data used to train an AI is biased, the AI might make unfair decisions. In relation to that there is another problem called Black Box problem when the reasoning behind AI decisions is challenging to understand.
3. Dependence
Too much reliance on anything is risky. It’s the same with AI as well, while it is helpful, it’s important to check it’s work under human expertise.
Now, there’s another concern with the use of AI that might take humans jobs. And as AI is getting better, that is a valid reason to worry.
So, at last these are the few things to keep in mind while using AI safely:
1. Making sure that AI systems are trained on fair and unbiased data.
See less2. By regularly testing AI systems to find and fix mistakes.
3. Combining human expertise with AI to make better decisions.
4. And to be aware of the potential risks and limitations of AI.