Cybersecurity has always been a game of adaptation. Every time defensive efforts are stepped up more sophisticated threats emerge, resulting in an ongoing arms race that businesses are under constant pressure to win if they are to secure their data and protect their reputation.
That pressure is now so great that traditional security methods can’t keep pace, particularly when attackers are leveraging AI to create malware and automated campaigns that are designed specifically to evade detection efforts. That pressure is also being felt – in a survey of enterprise risk professionals conducted by Gartner, AI-enhanced malicious attacks were ranked as the number one emerging risk throughout 2024.
However, in the spirit of fighting fire with fire, AI also offers businesses powerful new defensive capabilities. By applying sophisticated algorithms to analyze enormous volumes of DNS insights (domain name system) and network telemetry data, security teams can uncover hidden patterns, identify anomalies early, and predict attacks before they materialize.
Crucially, AI-driven cybersecurity doesn’t just detect threats faster – it provides organizations with the strategic intelligence to anticipate malicious activity and disrupt attacker infrastructure long before it impacts operations. Instead of taking a defensive posture, businesses can turn the tables on attackers and actively preempt and neutralize the threats they pose, destabilizing them in the process.
The Cybersecurity Arms Race
Traditional cybersecurity approaches are reactive by design, typically identifying threats only after they have already infiltrated an organization’s network. This passive posture forces security teams into a perpetual game of catch-up, often responding only once the damage has begun to occur.
The reality is that threat actors now operate at machine speed, using AI tools to generate new malware variants and using DNS to orchestrate an industrial scale pipeline of scams and malware campaigns. With roughly 450,000 new malicious malware programs being discovered every day, organizations that continue to rely solely on firewalls, endpoint detection or secure web gateways will remain exposed and vulnerable.
Compounding this challenge is the increasingly sophisticated abuse of DNS infrastructure by cybercriminals. Malicious actors use DNS to conceal their activities, scale their operations, orchestrate attacks, and exfiltrate sensitive data undetected. Techniques such as DNS tunneling and registered-domain-generated algorithms (RDGAs) allow attackers to bypass network defenses and maintain persistence within compromised networks.
Without visibility and proactive defenses at the DNS layer, organizations will find themselves constantly outmaneuvered by attackers who effortlessly shift between thousands of malicious domains to sustain their campaigns.
The Dark Side: The Weaponization of AI
AI is generally viewed as a force for good, but as with any new technology or tool it’s only as good as the hands that wield it. Cybercriminals are now using the likes of generative AI and deepfake technology to craft incredibly convincing phishing campaigns, reducing the skill and language barriers involved in such attacks while dramatically increasing their volume.
Where once poorly written phishing emails raised suspicion, AI-generated content now convincingly mimics genuine communications to fool even the most keen-eyed users while being so prevalent that the alarm bells literally never stop ringing.
Fighting Fire With Fire: AI as a Defensive Tool
Threat detection has now become a “big data” problem. As such, the effectiveness of modern cybersecurity hinges on the ability to analyze and interpret enormous amounts of network telemetry, from firewall and endpoint activity to DNS queries and IP transit logs. Threat actors often hide malicious activities in plain sight, blending seamlessly into the immense flow of data generated by network infrastructures.
AI transforms this challenge into an opportunity – by combing through this data as scale, AI can identify subtle patterns and behavioral anomalies that human analysts alone would miss. DNS in particular has emerged as a powerful source of telemetry data, providing detailed insights into network transactions, attacker methods, and emerging threat signatures, all of which AI systems can swiftly interpret to identify malicious activities early.
Threat actors can also be profiled to help predict future attack types and vectors. By continuously learning from network activity, AI can identify suspicious patterns indicative of impending threats – such as the rapid registration and activation of new malicious domains – long before these domains become widely recognized.
Such predictive capabilities enable security teams to neutralize threats at their inception, significantly reducing the window of vulnerability and limiting the ability of attackers to climb through. In short, leveraging AI transforms DNS from merely a network utility into an essential, predictive security tool capable of neutralizing threats before they materialize.
Defensive AI in Action
AI may have levelled-up attacker infrastructure, but it’s also levelled up organizations’ defensive capabilities. For instance, in a documented case involving the ransomware group BlackCat, machine learning supported DNS defenses identified and blocked malicious domains 163 days before they appeared in commercial or public intelligence feeds.
Proactive approaches like this one prevent threats from ever reaching “patient zero,” dramatically reducing the risk of costly breaches and data exfiltration. Beyond proactive blocking, AI also streamlines threat detection and response processes, significantly boosting the efficiency of Security Operations Centers (SOCs).
Today’s SOC teams are suffering with what is widely being described as “alert fatigue” or burnout, often handling more than 10,000 security alerts daily. Intelligent automation driven by AI can alleviate the pressure on these teams by sifting through enormous volumes of network data to automatically identify patterns that are indicative of malicious activity.
Strategic Recommendations for Businesses
First and foremost, organizations must prioritize Protective DNS (PDNS) and DNS Detection & Response (DNS DR) as frontline defenses in their cybersecurity strategies. PDNS is increasingly recognized as a crucial layer of protection because it leverages AI to proactively analyze and block DNS queries associated with malicious domains, zero-day threats, and data exfiltration attempts.
Initiatives such as those led by the US Cybersecurity and Infrastructure Security Agency (CISA) and the UK’s National Cyber Security Centre (NCSC) have integrated Protective DNS services at a national level, demonstrating how AI-powered DNS strategies enhance national security by centrally protecting governmental institutions and critical sectors.
Organizations should also integrate AI-driven DNS security into broader “Zero Trust” architectures. Zero Trust advocates removing implicit trust from all network interactions, including DNS queries, ensuring that only verified, secure interactions occur within networks.
By incorporating AI-driven DNS threat analysis within Zero Trust frameworks, organizations gain more granular visibility into threats in real-time, dramatically reducing their exposure to threats. This integrated approach not only proactively defends against threats but also strengthens the organization’s broader security posture and the policies that define it.
In an era where cyberthreats are moving fast, businesses must move faster. In a business landscape now defined by AI, success against cybercriminals will not be measured by how quickly an attack is detected, but by how effectively emerging threats can be anticipated, disrupted, and rendered harmless before they even begin
