In almost every segment of our lives, AI (artificial intelligence) is now having a significant impact: it can provide better health diagnostics and treatments; detect and reduce the risk of financial fraud; improve inventory management; and come up with the right recommendation for a Friday night streaming movie. However, it can also be argued that some of the biggest impacts of AI relate to cybersecurity.
AI’s ability to learn, adapt and predict rapidly evolving threats has made it an indispensable tool for protecting businesses and governments around the world. From basic applications like spam filtering to advanced predictive analytics and AI-assisted responses, AI plays a vital role on the front lines, defending our digital assets against cybercriminals.
The future of AI in cybersecurity isn’t all rainbows and roses, however. Today, we can see the first signs of significant change driven by the democratization of AI technology. While AI continues to empower organizations to build stronger defenses, it also provides threat actors with tools to craft more sophisticated and stealthy attacks.
In this blog, we will examine the evolving threat landscape, trace the evolving role of AI in cyber defense, and consider the implications for defending against attacks of the future.
AI in cybersecurity: the first wave (2000-2010)
As we welcome the new millennium, the first stages of digital transformation have begun to affect our personal and professional lives. In most organizations, knowledge workers performed their work in tightly managed IT environments, leveraging desktop and laptop computers, as well as on-premises data centers that formed the backbone of the organization’s IT infrastructure. organization.
The cyber threats that rose to prominence during this era were primarily aimed at causing chaos and gaining notoriety. The early 2000s saw the rise of malware like ILOVEYOU, Melissa, and MyDoom, which spread like wildfire and caused significant disruption on a global scale. By the mid-2000s, the lure of financial gains led to a proliferation of phishing schemes and financial malware. The Zeus banking Trojan has emerged as a significant threat, stealthily stealing banking credentials from unsuspecting users.
Organizations rely heavily on basic security controls, such as antivirus software and signature-based firewalls, to try to ward off intruders and protect digital assets. The concept of network security has begun to evolve, with improved intrusion detection systems entering the cybersecurity arsenal. Two-factor authentication (2FA) gained traction around this time, adding an extra layer of security for sensitive systems and data.
This is also when AI began to show significant value to defenders. As spam volumes exploded, unsolicited – and often malicious – emails clogged email servers and inboxes, tempting users with get-rich-quick schemes, illegal pharmaceuticals and similar lures to encourage them to reveal valuable personal information. Although AI still sounded like science fiction to many IT professionals, it has proven to be an ideal tool for quickly identifying and quarantining suspicious messages with previously unimaginable efficiency, helping to significantly reduce risk and to recover lost productivity. Although still in its infancy, AI has shown glimpses of its potential to help organizations protect against rapidly evolving threats at scale.
AI in cybersecurity: the second wave (2010-2020)
As we enter the second decade of the millennium, the makeup of IT infrastructure has changed significantly. The explosion of SaaS (software-as-a-service) applications, cloud computing, BYOD (bring your own device) policies and the emergence of shadow IT have made the IT landscape more dynamic than ever. At the same time, this has created an ever-expanding attack surface for malicious actors to explore and exploit.
Threat actors have become more sophisticated and their goals have broadened; Intellectual property theft, infrastructure sabotage, and larger-scale monetization attacks have become commonplace. More and more organizations have become aware of state threats, led by well-funded and highly sophisticated adversaries. This has created the need for equally sophisticated defenses that can learn autonomously and quickly enough to stay ahead of the curve. Incidents such as the Stuxnet worm targeting Iranian nuclear facilities and devastating attacks on high-profile companies like Target and Sony Pictures have gained notoriety and highlighted the escalating stakes.
At the same time, the vulnerability of supply chains has been exposed, as evidenced by the SolarWinds breach which impacted tens of thousands of organizations around the world. Perhaps most notably, ransomware and wiper attacks have increased with notorious strains like WannaCry and NotPetya wreaking havoc on a global scale. Although relatively easy to detect, the volume of these threats required defenses that could scale with a speed and precision far beyond the capabilities of a human analyst.
In this era, AI has become an indispensable tool for defenders. Leading the charge was Cylance, founded in 2012 to replace heavy legacy antivirus software with lightweight machine learning models. These models have been trained to quickly and effectively identify and stop fast-moving malware. The role of AI in cybersecurity has continued to expand, with machine learning techniques used to detect anomalies, report unusual patterns or behaviors indicative of a sophisticated attack, and perform predictive analytics to predict and prevent attacks. possible attack vectors.
AI in cybersecurity: the third wave (2020 to present)
Today, a profound change is taking place around the use of AI in cybersecurity. The ubiquity of remote work, coupled with hyperconnected and decentralized IT systems, has blurred the traditional security perimeter. With the rise of IoT (Internet of Things) and connected devices – from smart homes to smart cars to entire cities – the attack surface has expanded exponentially.
In this context, the role of AI has evolved from a simple defensive mechanism to a double-edged sword, also used by adversaries. While commercial generative AI tools, such as ChatGPT, have attempted to create guardrails to prevent bad actors from using the technology for malicious purposes, adversarial tools such as WormGPT have emerged to fill the void left to the attackers.
Potential examples include:
- AI-generated phishing campaigns: With the help of generative AI, attackers can now create highly convincing phishing emails, making these deceptive messages increasingly difficult to identify. Recent research also confirms that generative AI can save attackers days of work on every phishing campaign they create.
- AI-assisted target identification: By leveraging machine learning algorithms to analyze social media and other online data, attackers can more effectively identify high-value targets and tailor their attacks accordingly.
- AI-powered behavioral analysis: AI-powered malware can learn typical user or network behaviors, enabling attacks or data exfiltration that evade detection by better mimicking normal activity.
- Automated vulnerability scanning: AI-based reconnaissance tools can facilitate autonomous analysis of network vulnerabilities, automatically choosing the most effective exploit.
- Intelligent data sorting: Instead of mass copying all available data, AI can identify and select the most valuable information to exfiltrate, further reducing the chances of detection.
- AI-assisted social engineering: Using AI-generated deepfake audio or video in vishing attacks can convincingly impersonate trusted people, lending greater credibility to social engineering attacks that persuade employees to reveal sensitive information.
The deployment of this third wave of AI highlights a crucial inflection point in cybersecurity. The dual use of AI – both as a shield and a spear – highlights the need for organizations to stay informed.
Conclusion
The evolution of cybersecurity emphasizes the relentless ingenuity of threat actors and the need for defenders to remain well-equipped and informed. As we move to a phase where AI serves as both an ally and a potential adversary, the story becomes more complex and fascinating.
Cylance® AI has been there since the beginning, as a pioneer of AI-driven cybersecurity and as proven leader on the market. For the future, we BlackBerry® We continually push the boundaries of our Cylance AI technology to explore the future. Keep an eye out for our next blog where we will explain how generative AI comes into play as a powerful tool for defenders, providing a new perspective to anticipate and counter tomorrow’s sophisticated threats.
The future is very bright for those ready to embrace the evolution of AI-driven cybersecurity.
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Note – This article was expertly written by Jay Goodman, Director of Product Marketing at BlackBerry.
