We’ve talked about robots and how they can take over the world. We’ve seen apocalyptic movies and novels that pit man versus machine in epic fights of good versus evil. Current geopolitical and environmental issues have evolved in maturity and capability where cyber defense/and cyber offensive strategies are being used not only in military confrontations, but corporate boardrooms as well. As we think about the current cryptocurrency meltdown, economic and supply chain instability, we face a new dimension of threats that require rethinking and developing new out-of-the-box concepts to identify and defeat adversarial technologies.
Consider artificial intelligence (AI) and what a long way it has come during the past five years. Attending the ISC West show at that time, one encountered manufacturers and security integrators that touted capabilities within video surveillance and access control products with proactive detection and video analytics that were AI-based.
At that time, clarification and contrast examples were needed to understand AI from cognitive learning, decision support, deep learning, neural networks and adaptive reasoning systems — including how they assisted us with gathering useful information, but not really using AI-based systems.
Fast forward, AI became a household technology and industry giants Apple and Amazon (Siri, Alexa) cornered the market with real AI using natural language processing and interpretation. While still flawed in many situations, these products brought real value to the masses and became the cornerstone of prolific advanc.es in AI. The technology advanced from cell.phones being recognized as personal data assistants (PDAs) to bringing to life what is known as virtual assistants.
Let’s dig deeper into where we find ourselves today and what it portends for the electronic security industry and systems integrators.
Machine learning and AI presents a new cyberattack surface requiring new skill sets, technologies,and competencies to identify and mitigate cybersecurity risks to public, industry,and the nation.
Cybersecurity efforts aim to protect computing systems from digital attacks, which are a rising threat in the Digital Age. Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in AI and machine learning.
The monitoring of AI-based technologies, or adversarial AI, is based on technology that if used for malicious purposes can endanger public safety, health, and national security. Before we go down the path leading to machines that decide to exterminate mankind such as seen in movies like “The Terminator,” let’s talk about real-world examples.
One of the best examples we see today is what is known as deepfake. This refers to the use of AI-based models and computer algorithm capabilities to simulate voice, expressions, facial recognition, and computer vision facial images contextually so that the ability to discern real versus fake is not possible.
AI natural language models that now flood the Internet as chatbots, or bots, have been successfully programmed to alter data and make mistakes in its algorithms in advance of presenting it back to other systems and responses.
Without going into a dissertation of how AI is developed, there is a specific process known as training AI. This involves taking a machine algorithm and developing responses that classify how the input is used, processed, and output.
Most AI cyber-attacks on AI frequently use poisoning to train the data and labels to underperform during the deployment. Think of this as data being contaminated, rendering the data collected and processed through the AI system useless.
Another type of AI cyber-attack is AI model extraction. This is where there is the intent to steal the AI module and reconstruct the data to respond alternatively to the way it was intended to respond.
At the top of the list for dangerous potential targets of compromised AI are autonomous weapons systems, next brain-computer interfaces (BCI), self-driving vehicles, 3D printing, facial recognition, augmented reality, swarm intelligence, in addition to the already alluded to deep fakes and bots.
Distinctions need to be made between systems referred to as automated versus autonomous. Automated systems perform repetitive processes that reduce human interaction yet are still governed by processes and relationships managed by humans
Using strong digital encryption and cybersecurity best practices must always be top of mind in the shadow of defending against the formidable capabilities and power of AI. Governing the use of AI by the government, as well as public and private organizations, is not too far off.
The military perspective against weapons of war is to establish international guidance and laws of how, when, and under what conditions AI can be used in conflict.
The U.S. Department of Defense’s “Unmanned Systems Integrated Roadmap” sets out a concrete plan to develop and deploy weapons with ever-increasing autonomy in the air, on land, and at sea in the next 20 years. A defining feature of these autonomous weapons systems (AWS) is precisely their ability to operate autonomously: “robotic weapons … once activated, can select, and engage targets without further human intervention.”
As we seek to have greater nonmilitary privacy controls and protection of personally identifiable information (PII), there are specific methods that can be implemented to secure and train AI models. One such process is known as Privacy Preserving Machine Learning (PPML). Another involves code obfuscation techniques that incorporate face blurring with computer vision recognition models.
Making sure that AI is fully and completely aligned to human goals is surprisingly difficult and takes careful programming. AI with ambiguous and ambitious goals are worrisome, as we don’t know what path it might decide to take to its given goal
One thing is certain … we saw it coming! SSI
President and CEO of SecureXperts