“Elevation, Morpheus, Evolution, like the dinosaurs.” The words uttered by the villainous Agent Smith in “The Matrix,” perfectly describe today’s cybersecurity landscape. Failure to quickly act will end in disaster, the threats are multiplying, with the bad actors using AI to launch and target assets. Some of the most common threats include the following:
- Data leakage: Sure, AI is helping ease the workload, but is it safe? The human tendency to let the machine do the job means there is every chance of the leakage of confidential data. The risk multiplies when organizations integrate AI into their workflows and can lead to violation of multiple data privacy laws and incur unnecessary noncompliance and the resulting fallout, including financial and brand image damages.
- AI-aided identity frauds: AI is allowing people to build deepfakes, which can be used to spread disinformation, criminal impersonations, and commit financial crimes through impersonations. The proliferation of AI and the increasing sophistication of deepfakes means that traditional identity tools are not capable of accurate detection. This is a multi-pronged weapon utilizing both identity and voice channels. The risk is bad enough that the FBI has issued a warning informing how criminals are misusing AI. There is a need for adding extra layers for identity verification, as the facial and auditory prompts may not now be enough for verification. The scary efficiency in distorting AI chatbots has been witnessed since 2016, and the increased adoption of such bots just expands the risk a lot.
- Poisoning/manipulation of LLMs: AI “learns” through LLMs. But what if the models themselves are fed malicious or bad data? It essentially means that the output will be biased in favor of the attackers and will lead to a rise in misinformation. It can also “tell” the AI to ignore malicious data or links and let them slip through. It can also be used to slow down or degrade the model’s performance.
- AI-generated malware and data processing: Any technology is used by both good and bad actors. The same goes for AI technology. It can be used to write polymorphic or mutating malware, which can evade endpoint detection systems. The scary part about this is that because of AI’s self-learning capability, increasingly sophisticated malware can be created. The actors can also run analytics on stolen data, enabling faster data processing and consequently increasing the problems facing the entity the data is stolen from.
- Poisoned prompts: Ever heard of “jailbroken AI?” Most of the time, it is AI that has gone in a completely different direction than the originally imagined one. This issue has been creating havoc for a long time. Microsoft introduced a chatbot named Tay in 2016. It was supporting Hitler and denying the holocaust. It is not a difficult task if you know what kind of prompts you need to give to the software. Once the software has successfully breached its safety rules, it can be used for anything, including building bombs, hacking, and building malware.
Now that we know more about the threats you can face while using AI, let us see how a security architect can help you stave off the trouble. The architect is primarily responsible for maintaining the security of an organization’s AI assets. The person is basically responsible for designing, securing, and overseeing AI/ML infrastructure, models, and data pipelines. In other words, a security architect provides security to tools used to provide security to the organizational assets. The principle tasks performed by a security architect include:
- Protecting AI tools used in defensive cybersecurity (e.g., anomaly detection) aren’t subverted or tricked.
- Enterprise AI deployments don’t expose sensitive data or become new attack surfaces.
- AI-generated content is trustworthy and traceable (mitigating hallucinations or misinformation).
The next question people will have is “My SecOps team already has security engineers, why do I need another specialist?’ Well, they may not have knowledge about model architecture, ML pipelines, embedding spaces, training data vulnerabilities, and drift detection. This knowledge gap poses the danger of conventional security teams missing AI-specific risks.
As AI adoption grows, as is the norm, so does the addition of newer threats. AI is already being used for cyberattacks. Therefore, organizations need to develop a defense against such attacks. This is why you need an AI security architect.