The wide adoption of Artificial Intelligence (AI) has transformed business and individual operations in disruptive ways. Cybersecurity ranks high among the domains seeing massive evolutionary improvements due to these advancements.
However, this evolution has not been a one-way street. Thanks to AI, attackers have become even more destructive in exploiting potential vulnerabilities of the cyber ecosystem—making it significantly difficult for defenders to catch. All of this raises an important question–has AI become a double-edged sword? Let’s find out.
AI for the Worse—Cyber Attacks
Led by AI, attackers are posing tremendous challenges in keeping the cyber ecosystem secure from vulnerabilities:
- Social Engineering
Cyber attackers have become more deceptive, leveraging social engineering campaigns. Through access to a large amount of data, the attackers are executing large-scale attacks with ease in addition to using the data to spread misinformation online.
The large data set enables attackers to target specific individuals–be it through zones, keywords, geographical information, or language distinctions. Gathering various information through social media sources, the messages crafted by the attackers have non-existent grammatical errors and target the most vulnerable individuals who fall for these messages.
- Exploitation through Automation
AI has made the exploitation of vulnerable systems more efficient by integrating AI into the cyber attack kill chain. With malicious intent, automated tools scan large volumes of data to find potential weaknesses led by the development of exploits, zero-day attacks, and malevolent software.
- More Sophistication
AI has made attackers more sophisticated than ever. A prime example of this is the use of search engine advertisements as vectors. In many cases, the attackers impersonate large global financial institutions and direct the victims to malicious websites.
- Data Manipulation
Attackers have begun manipulating data consumed by the AI algorithms, which eventually results in AI algorithm manipulation. The attackers do this by feeding information to algorithms through legitimate yet compromised sources. Causing the algorithm to error, the attackers make the algorithm put out incorrect information—diverting the purpose of these algorithms.
AI for the Better—Cyber Defense
- Boosted Network Analysis
Similar to attackers, AI-powered security systems can also analyze large amounts of data to identify patterns that might cause cyber attacks, providing a well-rounded approach to threat detection. Machine learning algorithms are trained to recognize intrusion signs before they cause any substantial damage.
AI systems proficiently monitor networks for unusual activity that could potentially reveal a security breach. Through constant analysis of network traffic, these systems can point out abnormal patterns—unusual login times, high data traffic, or unrecognized IP addresses.
- Automated Threat Detection
Machine algorithms have become very accurate at identifying genuine threats. By leveraging predictive analysis, AI can forecast potential vulnerabilities and attack vectors before they are exploited. It allows organizations to repair security gaps and carefully protect them before attackers find these loopholes.
- Predictive Analysis
AI-powered predictive analytics understand normal user behavior and entities within a network. Through deep learning, AI aims to distinguish legitimate user actions and potential threats through abnormal behaviors such as sudden changes in file access patterns or data transfer volumes—underlining a compromised account or a threat from the inside.
- Cyber Triage
Triaging has significantly enhanced and increased the capabilities of cyber defenders. By navigating large data sets and identifying potential threats, AI enables human analysts to focus on complex tasks—threat-hunting and forensic analysis, merging information from different sources, and executing strategic security planning.
Tools to Tackle the AI-Driven Attacks
To defend against attackers who are misusing AI, combining advanced tools and tech is necessary. Here are some of the solutions one can leverage: EDR systems, SIEM platforms, and AI-based threat intelligence platforms, like Juniper Mist. However, it is crucial to know that there is no one-size-fits-all tool that will offer complete security, there is no human insight is still an essential part of leveraging any tool.
Regulatory and Resource Challenges
As the AI landscape continues to evolve, it has also given birth to a myriad of difficulties that highlight the need of the hour to govern AI applications with an adaptable framework. Currently, the issues related to AI revolve around fairness, accountability, and transparency.
On the other hand, generative AI isn’t non-biased either and could unfairly favor certain demographics over others, highlighting the need for regulatory oversight to ensure fair and unbiased practices.
As numerous sectors such as cybersecurity are adopting AI into their operations, the potential for challenging situations also increases. With regulatory frameworks, AI technology can be developed and deployed collectively in a way that adheres to one standard. Notably, since most attackers aren’t concerned about regulations, it could potentially make the gap between cyber attackers and cyber defenders even bigger than before.
Globally, there is a lack of expertise in terms of professionals skilled in AI-driven security. Syncing AI with the right software can boost response time and improve overall security, as the teams working on security will have more bandwidth thanks to AI.
Future of AI in Cybersecurity
No matter what, the fundamentals of AI such as patching vulnerabilities, running regular scans, and strengthening endpoint security remain a crucial part of cybersecurity. Potentially. looking at the pace of the current AI development, the dynamics between the attackers and defenders will be stretched even further as one side innovates and the other adapts–a continuous cycle.
With AI integration across the sphere, experts are championing robust yet enforceable policies. While such policies are vital, it is also challenging to balance innovation and the implementation of required regulations for a secure future. On the other hand, to mitigate risk, organizations must stay updated with the latest AI advancements.
