Emile Abou Saleh, the Senior Director for Middle East, Turkey and Africa at Proofpoint, says a proactive approach to cybersecurity robustly protects organizations against a wide range of threats in an increasingly complex digital landscape
Generative AI has gained considerable attention in the news lately, and like any new technology, there’s a lot of excitement around it. Today’s Generative AI tools go beyond traditional chatbots; they are becoming more advanced. Generative AI’s potential reaches far and wide, benefiting professionals across different industries. Financial advisers can use it to analyze market trends, educators can tailor lessons to students’ needs, and it’s also proving useful in the field of cybersecurity. Security analysts can leverage Generative AI to examine user behaviour and detect patterns that could indicate potential data breaches.
One of the standout features of Generative AI in cybersecurity is its ability to quickly and accurately process vast amounts of data related to emerging threats. Security administrators can use these tools to run queries quickly, and in just a few minutes, these tools can summarize current credential compromise threats and highlight specific indicators to watch out for.
Why according to you should cybersecurity companies leverage generative AI?
Our lives and work cultures are forever changed, with so many people working and interacting digitally—and the velocity of business and the volume of corporate data we generate growing exponentially, across multiple digital platforms.
Many organizations across all industries have found that implementing artificial intelligence (AI) into business systems has helped them to ensure continuity, with one main aspect being increased productivity. When looking at this from a cybersecurity point of view, there are many ways AI and machine learning (ML) can bolster an organization’s overall cybersecurity posture.
Today’s threat landscape is characterized by attackers preying on human vulnerability. Proofpoint research shows that nearly 99% of all threats require some sort of human interaction. Whether it is malware-free threats such as the different types of Business Email Compromise (BEC) or Email Account Compromise (EAC) like payroll diversion, account takeover, and executive impersonation, or malware-based threats, people are falling victim to these attacks day-in and day-out. And all it takes is one click, from one employee for a threat actor to infiltrate an organization’s systems, no matter how complex the environment.
To stop these types of attacks, organizations need to deploy a security solution that can stay ahead of the ever-changing landscape and adapt to the way humans act. AI and ML are critical components in a robust cybersecurity detection strategy. It’s faster and more effective than manual analysis and can quickly adapt to new and evolving threats and trends. Cybersecurity defences that employ AI can combat such threats with greater speed, relying on data and learnings from previous, similar attacks to predict and prevent their spread.
What are the cybersecurity challenges facing companies with the adoption of AI and how can they be overcome?
With the adoption of AI, organizations face a set of cybersecurity challenges that need immediate attention. While AI has shown remarkable progress in defending against common threats, it has also opened doors for cybercriminals.
Take phishing: AI has the potential to supercharge this threat, increasing the speed and accuracy in which these phishing emails are sent to victims. However, it’s important to remember that many social engineering emails aren’t designed to be “perfect” – they are intentionally written poorly to find people who are more likely to engage.
That’s also only one part of the threat. Headers, senders, attachments, and URLs are among the many other threat indicators that are analyzed by robust detection technologies. Even where there would be a substantial benefit to having better-crafted emails, like many business email compromise scenarios, there is a lot of other information the threat actor needs to have access to. They need to know who is paying what money to whom and at what dates, which they probably have already accessed in a different way. They don’t necessarily need AI assistance when they already have access to that person’s inbox and they can merely copy an old email.
It’s crucial for organizations to note that no matter the attack vector, or how complex it is, the majority of cyberattacks require human interaction to be successful. By tricking just one employee, threat actors can circumvent security tools and siphon sensitive corporate data. Organizations must implement a people-centric cybersecurity strategy, consistently training employees at all levels of the business, in cybersecurity best practices so they are aware of the latest cyber threats and are able to detect them, report them, and not fall victim to them.
How can organizations use their resources effectively to leverage Gen AI to gain a competitive edge in the cybersecurity landscape?
To effectively leverage Generative AI and gain a competitive advantage in the cybersecurity landscape, organizations should focus on two vital aspects. It is firstly essential to embrace a people-centric security model for data loss prevention, acknowledging that individuals often play a pivotal role in the movement of data. This approach encompasses content awareness, behavioural analysis, and threat awareness, granting in-depth insights into how employees interact with sensitive data.
Increased visibility facilitates real-time detection and prevention of data loss incidents. Secondly, organizations should integrate artificial intelligence (AI) and machine learning (ML) technologies into their cybersecurity practices. For instance, in email security solutions, AI and ML swiftly identify and thwart phishing campaigns, malicious URLs, imposter messages, and unusual user activity in cloud accounts. A proactive approach to cybersecurity robustly protects organizations against a wide range of threats in an increasingly complex digital landscape.