While viruses and malware have stubbornly stayed as a top-10 “things I lose sleep over as a CISO,” the overall threat has been steadily declining for a decade. Unfortunately, WannaCry, NotPetya, and an entourage of related self-propagating ransomware abruptly propelled malware back up the list and highlighted the risks brought by modern inter-networked business systems and the explosive growth of unmanaged devices.
The damage wrought by these autonomous (not yet AI-powered) threats should compel CISOs to contemplate the defenses to counter such a sophisticated adversary.
The threat of a HAL-9000 intelligence directing malware from afar is still the realm of fiction, so too is the prospect of an uber elite hacker collective that has been digitized and shrunken down to an email-sized AI package filled with evil and rage. However, over the next two to three years, I see six economically viable and “low hanging fruit” uses for AI infused malware – all focused on optimizing efficiency in harvesting valuable data, targeting specific users, and bypassing detection technologies.
• Removing the reliance upon frequent C&C communications – Smart automation and basic logic processing could be employed to automatically navigate a compromised network, undertake non-repetitive and selective exploitation of desired target types and, upon identification and collection of desired data types, perform a one-off data push to a remote service controlled by the malware owner. While not terribly magical, such AI-powered capabilities would not only undermine all perimeter blacklist and enforcement technologies, but also sandboxing and behavioral analysis detection.
• Use of data labeling and classification capabilities to dynamically identify and capture the most interesting or valuable data – Organizations use these types of data classifiers and machine learning (ML) to label and protect valuable data assets. But attackers can exploit the same search efficiencies to find the most valuable business data being touched by real users and systems and to reduce the size of data files for stealthy exfiltration. This enables attackers to sidestep traffic anomaly detection technologies as well as common deception and honeypot solutions.
• Use of cognitive and conversational AI to monitor local host email and chat traffic and to dynamically impersonate the user – The malware’s AI could insert new conversational content into email threads and ongoing chats with the objective of socially engineering other employees into disclosing secrets or prompting them to access malicious content. Since most email and chat security solutions focus on in-bound and egress content, internal communication inspection is rare. Additionally, conversational AI is advancing quickly enough to make socially engineering IT helpdesk and support staff into disclosing secrets or making temporary configuration a high probability.
• Use of speech to text translation AI to capture user and work environment secrets –Through a physical microphone, the AI component could convert all discussions within range of the compromised device to text. In addition, some environments may enable the AI to successfully capture the keystrokes of nearby systems and deduce what keys are being pressed. Such an approach also allows hackers to be more selective of what secrets to capture, further minimizing the volume of data that must be egressed from the business, which then reduces the odds of triggering network-based detection technologies.
• Use embedded cognitive AI in applications to selectively trigger malicious payloads – Since it is possible for cognitive AI systems to not only recognize a specific face or voice, but also determine their race, sex, and age, it is therefore possible for a malware author to be very specific in who they choose to target. Such malware may only be malicious for the CFO of the company or may only manifest itself if the interactive user is a pre-teen female. Because the trigger mechanism is embedded within complex AI, it becomes almost impossible for automated or manual investigation processes to determine the criteria for initiating the malicious behaviors.
• Capture the behavioral characteristics and traits of system users – AI learning systems could observe the unique cadence, timbre, and characteristics of the users typing, mouse movements, vocabulary, misspellings, etc. and create a portable “bio-profile” of the user. Such “bio-profiles” could then be reused by attackers to bypass the current generation of advanced behavioral monitoring systems that are increasingly deployed in high security zones.
These AI capabilities are commercially available today. Collectively or singularly, each AI capability can be embedded as code within malicious payloads.
Because deep neural networks, cognitive AI, and trained machine language classifiers are incredibly complex to decipher, the trigger mechanism for malicious behaviors may be deeply buried and impossible to uncover through reverse engineering practices.
The baseline for defending against these attacks will lie in ensuring all parts of the organization are visible and continually monitored. In addition, CISOs need to invest in tooling that brings speed and automation to threat discovery through AI-powered detection and response.
As malware writers harness AI for cybercrime, the security industry must push forward with a new generation of dissection and detonation technologies to prepare for this coming wave. A couple promising areas for implementing defensive AI include threat intelligence mining and autonomous response (more on this later).