Offensive Cyber Series: Amy Ertan on AI and Military Innovation, Part I
On Wednesday 17th March, Strife Interviewer Ed Stacey sat down with Amy Ertan to discuss offensive cyber in the context of artificial intelligence (AI) and military innovation. For part three of Strife’s Offensive Cyber Series, Ms Ertan discusses the current role of AI in offensive cyber and potential future trajectories, including effects on the offence-defence balance and arms racing, as well as her PhD research, which explores the unforeseen and unintended security consequences of developing and implementing military AI.
Ed Stacey: Amy, could you start by briefly defining AI in the context of offensive cyber. Are we really just talking about machine learning, for example?
Amy Ertan: Artificial intelligence is not just machine learning algorithms – it is a huge range of technologies. There is a whole history of AI that goes back to before the mid-1970s and late-80s: rule-based AI and knowledge-based AI which is, as it sounds, learning based on rules and logic. Then in the last decade or so we have seen a huge uptick in machine learning-based algorithms and its various sub-branches, including deep-learning and neural networks, which are incredibly complex algorithms that we cannot actually understand as humans. So, in summary, AI is a big umbrella term for different kinds of learning technologies.
At the same time, there is some snake oil in the market and a lot of what people call AI can just be probabilistic statistics. Being generous, some of the start-ups that you see are doing if-then algorithms that we could probably do on Excel. That does not, of course, account for the tech giant stuff. But when we talk about AI, we have everything from the super basic things that are not really AI to the incredibly well-financed, billion dollar projects that we see at Amazon, Microsoft and so on.
Machine learning is where a lot of today’s cutting edge research is. So the idea that you can feed data, potentially untagged data – unsupervised learning – into an algorithm, let the algorithm work through that and then make predictions based on that data. So, for example, you feed in three million pictures of cats and if the algorithm works as intended, it will then recognise what is and is not a cat.
In terms of how that fits into offensive cyber, AI is another tool in the toolkit. A learning algorithm, depending on how it is designed and used, will be just like any other cyber tool that you might have, only with learning technology within it. I would make the point that it is not something that we see being utilised today in terms of pure cyber attacks because it is not mature enough to be creative. The machine learning AI that we have right now is very good at narrow tasks, but you cannot just launch it and there is no “AI cyber attack” at the moment.
ES: How might AI enhance or facilitate offensive cyber operations?
AE: As I said, AI is not being used extensively today in offensive cyber operations. The technology is too immature, although we do see AI doing interesting things when it has a narrow scope – like voice or image recognition, text generation or predictive analytics on a particular kind of data set. But looking forward, there are very feasible and clear ways in which AI-enabled technologies might enhance or facilitate cyber operations, both on the offensive and defensive side.
In general, you can talk about the way that AI-enabled tools can speed up or scale up an activity. One example of how AI might enhance offensive cyber operations is through surveillance and reconnaissance. We see already, for example, AI-enabled tools being used in intelligence processing for imagery, like drone footage, saving a huge amount of time and vastly expanding the capacity of that intelligence processing. You could predict that being used to survey a cyber network.
Using AI to automate reconnaissance, to do that research – the very first stage of a cyber attack – is not a capability that you have now. But it would certainly enhance a cyber operation in terms of working out the best target at an organisation – where the weak link was, the best way in. So there is a lot that could be done.
ES: Are we talking then about simply an evolution of currently automated functions or does AI have the potential to revolutionise offensive cyber?
AE: In terms of whether AI will be just a new step or a revolution, generally my research has shown that it will be pretty revolutionary. AI-enabled technology has the power to revolutionise conflict and cyber conflict, and to a large extent that is through an evolution of automated functions and autonomous capabilities. I think the extent to which it is a full-blown revolution will depend on how actors use it.
Within cyberspace, you have this aspect that there might be AI versus AI cyber conflict in the future. Where your offensive cyber tool – your intrusion, your exploit tool – goes head-to-head with your target’s AI-enabled cyber defence tools, which might be intrusion prevention or spam filtering tools that are already AI-enabled. It really depends on how capabilities are used. You will have human creativity but then an AI algorithm makes decisions in ways that humans do not, so that will change some aspects of how offensive cyber activity takes place.
There is debate as to whether this is a cyber attack or information warfare, but I think deep fakes would be an example of a technology or tool that is already being used, falsifying information, that has revolutionised information warfare because of the scale and the nature of the internet today. So how far AI revolutionises offensive cyber will depend not only on its use but also a complex set of interconnections between AI, big data, online connectedness and digital reliance that will come together to change the way that conflict takes place online.
That is a complicated, long answer to say: it depends, but AI definitely does have the potential to revolutionise offensive cyber.
ES: No, thank you – I appreciate that revolutionary is a bit of a loaded term.
AE: Yes, there is a lot of hyperbole when you talk about AI in warfare. But through my doctoral research, every industry practitioner and policy-maker that I have spoken to has agreed that it is a game-changer. Whether or not you agree with the hype, it changes the rules of the game because the speed completely changes and the nature of an attack may completely change. So you definitely cannot say that the power of big data and the power of AI will not change things.
ES: This next question is from Dr Daniel Moore, who I spoke to last week for part two of this series. He was wondering if you think that AI will significantly alter the balance between offence and defence in cyberspace?
AE: I am going to disappoint Danny and say: we do not know yet. We do already see, of course, this interesting balance that states are choosing when they pick their own defence versus offence postures. And I think it is really important to note here that AI is just one tool in the arsenal for a team that is tasked with offensive cyber capabilities. At this point, I do not predict it making a huge difference.
At least when we talk about state-coordinated offensive cyber – sophisticated attacks, taking down adversaries or against critical national infrastructure, for example – they require such sophisticated, niche tools that the automation capabilities provided by AI are unlikely to offer any cutting-edge advantage there. So that depends. AI cyber defence tools streamline a huge amount of activity, whether that is picking out abnormal activities in your network or your logs, that eliminates a huge amount of manual analysis that cyber defence analysts might have to do and gives them more time for meaningful analysis.
AI speeds up and streamlines activity on both the offensive and defensive side, so I think it simply fits into the wider policy discussions for a state. It is one aspect but not the determining aspect, at the moment anyway or in the near future.
ES: And I guess the blurring of the lines between offence and defence in some cyber postures complicates the issue a little?
AE: Yes, especially when you look at the US and the way they define persistent engagement and defending forward. It is interesting as to where different states will draw their own lines on reaching outside their networks to take down the infrastructure of someone they know is attacking them – offensive activity for defensive purposes. So I think the policy question is much bigger than AI.
ES: Thinking more geopolitically, the UK’s Integrated Review was heavy on science and new technologies and other countries are putting a lot of resources into AI as well. There seems to be some element of a security dilemma here, but would you go so far as to say that we are seeing the start of a nascent AI arms race – what is your view of that framing?
AE: I think to an extent, yes, we do see aspects of a nascent AI arms race. But it is across all sectors, which comes back to AI as a dual-use technology. The Microsoft AI capability that we use now to chat with friends is also being used by NATO command structures and other military structures in command and control infrastructure, albeit in a slightly different form.
Because cutting-edge AI is being developed by private companies, which have the access and resources to do this, it is not like there is this huge arsenal of inherently weaponised AI tools. On the flip side, AI as a dual-use technology means that everything can be weaponised or gamed with enough capability. So it is a very messy landscape.
There have been large debates around autonomous systems in conflict generally, like drones, and I think there is an extent to which we can apply this to cyberspace too. While there is this security dilemma aspect, it is not in any states’ interests to escalate into full-blown warfare that cannot be deescalated and that threatens their citizens, so tools and capabilities should be used carefully.
Now there is a limit to how much you can apply this to cyberspace because of its invisible nature, the lack of transparency and a completely different deterrence structure. But there is an argument that states will show restraint in weaponizing AI where it is not in their interest. You see this conversation taking place, for example, around lethal autonomous weapons at the United Nations Group of Governmental Experts, where it is generally considered that taking the human out of the loop is highly undesirable. But it is complicated and early days.
Looking at the UK, my research has shown that there is pressure to develop AI capabilities in this space and there are perceptions of an AI arms race across the private sector, which is who I spoke to. And there is this awareness that AI investment must happen, in a large part because of anticipated behaviour of adversary states – the idea that other states do not have the same ethical or legal constraints when it comes to offensive cyber or the use of military AI, which is what my PhD thesis focuses on. The only preventative answer to stop this security mechanism building up into an AI arms race seems to be some kind of consensus mechanism, whereby like-minded states agree not to weaponize AI in this way. That is why my research has taken me to NATO, to look in the military context at what kinds of norms can be developed and whether there is a role for international agreement in this way.
If I had to summarise that argument into one or two sentences: there are trends suggesting that there is an AI arms race which is bigger than conflict, bigger than the military and bigger than cyber. So you have to rely on the security interests of the states themselves not to escalate and to potentially form alliance agreements to prevent escalation.
Part II of this interview will be published tomorrow on Friday 18th June 2021.
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