It’s not all just talk anymore. AI – especially of the generative kind – has pervaded organizations today, for good or bad.
Artificial intelligence (AI) has witnessed exponential growth since its inception in the 1950s, revolutionizing many industries and transforming the cybersecurity and data management landscapes.
While the 1956–1974 period could be considered the golden years of AI theory and research, the year 2023 can easily claim to be THE golden year of AI awareness and adoption.
Generative AI has been the talk of the town since the beginning of 2023, and the buzz hasn’t abated yet. If anything, AI and cyberthreats are the two technology topics dominating both formal and informal industry discussions today.
16 July is globally celebrated as AI Appreciation Day. This year, however, the celebrations were special, as AI’s popularity has multiplied in 2023 with the rise of the likes of ChatGPT, Google Bard and DALL-E. Almost every major development and application suite has incorporated AI into their offerings.
But so have cybercriminals and threat actors.
Here, we share some expert perspectives from two industry experts on challenges and strategies in leveraging the power of AI for better data management and dealing with AI-powered cyber-attacks.
Andy Ng, Vice President and Managing Director, Asia South and Pacific Region, Veritas Technologies:
The power of AI today marks an important inflection point for the technology industry. Coined by John McCarthy in the 1950s, AI has been evolving mostly in the backend for years before the emergence of ChatGPT – a generative AI tool – captured the imagination of many in late 2022.
While the true potential of AI is yet to be fully discovered, we can assume that its applications will be highly data intensive, creating the need for enterprises to deploy efficient and responsible data management.
However, in today’s multi-cloud world, companies often struggle to manage the massive data deluge as the traditional data management approaches prove inadequate due to the lack of scalability, speed and visibility. As a result, organizations are revisiting their business processes and looking to integrate AI into their data management strategies with the promising prospect of enhancing efficiency.
Done right, an organization’s AI strategy will be a regular and seamless part of its overall data management strategy.
The panacea for data management in a complex, hybrid, multi-cloud environment is to deploy autonomous data management (ADM) based on AI. For instance, with AI-driven malware scanning and anomaly detection, organizations can manage their data and automate protection from cyber threats such as ransomware. AI also enables the automation of data management processes, minimizing human intervention. This results in operational efficiencies, increased uptime, higher service levels and AI-driven insights for effective data archiving and intelligent decision-making.
The reliance on AI for data management also creates significant security risks if proper data framework is not in place, since AI-powered systems is dependent on large sets of data. Hence, it is critical for organizations to ensure the integrity of any data processes that leverage AI and take the necessary steps to defend against cyberthreats by implementing robust encryption, access controls, and authentication mechanisms.
Another crucial aspect is addressing biases in AI algorithms to prevent discriminatory outcomes and enhance fairness in decision-making processes. By effectively managing these security concerns and biases, organizations can unleash AI’s potential in data management to achieve transformative business outcomes.
Darren Reid, Senior Director, Security Business Unit, APJ, VMware:
The proliferation of AI has introduced a new dimension to cyberthreats, amplifying both the sophistication and scale of attacks. Malicious actors are increasingly manipulating AI-powered tools and techniques to launch targeted and evasive attacks that bypass traditional security measures.
The rapid advancement of AI-driven attacks poses significant challenges for cybersecurity, necessitating the development of AI-powered defenses and robust countermeasures to effectively detect, mitigate, and respond to emerging cyberthreats.
Training AI to counter cyberthreats presents a different set of challenges. In order for algorithms to be trained to tackle sophisticated AI-powered attacks, they must first learn from data sets that are representative of malicious behavior, which is data created specifically to avoid detection – this process is otherwise known as adversarial learning.
Moreover, it is difficult for AI to identify malicious programs with high precision as they will often generate errors known as “false positives”, thus often requiring human intervention.
AI-powered network detection and response (NDR) represents a robust approach to defending networks against advanced cyber threats. By leveraging the capabilities of artificial intelligence, NDR systems can significantly enhance the speed, accuracy, and effectiveness of detecting and responding to attacks. These systems analyze vast amounts of network traffic data, identify anomalies, and detect potential threats that may typically go unnoticed by traditional security measures.
The integration of AI also enables NDR solutions to provide comprehensive data gathering from multiple security technologies and automated incident response. This automation streamlines the incident response process, allowing security teams to focus on stopping actual intrusions. AI-powered NDR tools are constantly learning and adapting to allow for the automatic detection of advanced and ever-evolving threats, as well as automatically delivering a detailed analysis of the attack and response.
AI is not solely associated with negative aspects; it also brings forth numerous positive developments and advancements. While it may have resulted in more sophisticated cyber-attacks, organizations can ultimately leverage AI to counter these attacks, to strike the right balance between cybersecurity and innovation.