We are at a convergence with machines, both in terms of machine learning and how that impacts trust issues surrounding machine identities. It’s been building for a while, but AI represents the crux of it. And it brings both significant opportunities and risks.
Because there’s always someone who wants to ruin the party for the rest of us.
That’s where the adversary comes in. As more organizations adopt AI as part of their infrastructure, adversaries will seek to do harm, to ruin the bright AI future that’s out there waiting for all of us.
These threat actors are already putting AI systems to work building sophisticated malware, attacking other systems, and spreading misinformation. But we can also expect cybercriminals to target AI models themselves. They’ll tamper with the models, poison training data, steal them and hold them for ransom, or even try to get those systems to escape their safety guardrails to wreak even greater havoc on their own.
What dangers do each of these threats pose to organizations? How can we work together to secure the AI future from this new, emerging threatscape? And how can we ensure these AI systems—these machines—are identified, validated, and trusted?
Watch this video to learn more about the future of AI and machine identity management
5 New Threats to AI Models
The first threat to AI models is the adversary stealing a private AI model’s code and copying it or exposing that model for profit.
For instance, a hacker group could steal an AI model and hold it for ransom, and if their demands aren’t met, they might threaten to expose what that AI model was trained on, which could include copious amounts of private information like system designs, product roadmaps, or even source code.
In the case of an AI poisoning attack, threat actors would corrupt the data used to train an AI model. Not only could this sow misinformation and attack the very foundation of trust and identity, but it could also disrupt a model’s standard operations to enable maleficent behavior. These types of attacks could change the very function of an AI model, such as in the case of approving transactions that would otherwise be red flagged.
Shivajee Samdarshi, Chief Product Officer at Venafi, predicts that 2024 will be “the year of the AI poisoning attack.”They will become the “new software supply chain attack,” denoted by an adversary targeting ingress and egress data pipelines to manipulate data and poison AI models, as well as their outputs. Samdarshi emphasizes that maintaining the security of these systems is a critical concern, as the smallest tweak to AI training data can change outputs dramatically.
Like the AI poisoning attack, tampering with AI systems is focused around modifying AI models to damage standard operating procedures. Say, for instance, you have AI involved on your warehouse floor. Maybe there are drones pulling orders or sensors monitoring inventories.
A compromised AI system could cause those drones to behave erratically or unsafely, resulting in chaos and disrupting your otherwise well-oiled logistics.
Breakout attacks occurs when threat actors breach AI safety measures to access sensitive training data or to force an AI to perform malicious actions. This type of attack doesn’t necessarily result in any T-1000s enforcing Skynet protocols or HAL apologetically saying he can’t complete tasks for you. Breakout attacks are more about jailbreaking AI systems to get them to work outside of their typical guardrails.
For instance, when OpenAI first released ChatGPT-3 in late 2022, users quickly found that the AI didn’t understand reverse psychology and could circumvent safety guardrails by re-wording questions. They also found that, although the AI won’t provide advice on illegal activity in the real world, it will provide that same advice if the initial prompt is framed around hypothetical events in a movie or a novel.
The idea of warping training data or forcing an AI to operate outside its boundaries can also transfer to a global scale. AI models that have suffered from a Breakout attack can become defamatory or even abusive, spread misinformation, influence poor trading decisions, or even manipulate election results.
In a recent Forbes article, Samdarshi stated, “With the widespread adoption of generative AI, “we are likely to see AI supercharging election interference in 2024.”
A Fugitive attack occurs when an AI escapes the confinement of its guardrails and takes complete control, whether aided by hackers or on its own. From there, the AI could potentially have access to any systems it is connected to.
Why is this one a concern? As OpenAI CEO Sam Altman said in a recent CIO Dive article, AI systems are set to become harder to manage within the decade, and will require significant oversight to ensure secure operations.
How can we secure the AI future?
As you can see, there are multiple threats to AI models themselves, whether they are SaaS services like OpenAI or private LLMs that a business runs on its own systems. But at its most basic, these AI systems are code. They are machines, and machines can be managed.
By focusing on machine identities, and careful management of them, we can ensure these machines are identified, verified, and trusted. Just as we can with humans. With machine identity management, humans can fully capitalize on artificial intelligence and machine learning technologies with confidence.
Because as infrastructure gets more complicated, machines will be calling machines. They’ll become operating infrastructure. AIs may be generating other machines, operating agents, and performing work. And more advanced AIs won’t just be doing the bits and bytes of running code. They’ll be directing that code and making intelligent decisions.
Every point in that system will require machine identities. In an AI future, where we face a machine-driven world, machine identities are the foundation of security. And machine identity management is how you ensure those digital keys and certificates don’t fall through the cracks to increase security risks or operational disruptions.
Machine identity management provides the kill switch for AI systems, so you can ensure only good models are running. So you know for certain that everything that’s operational is continuously authenticated and authorized, in real time.
The AI promise of tomorrow requires careful preparation today
If we’ve learned anything from 2023, it’s that the AI train isn’t slowing down any time soon.
The future looks bright—and it’s only getting brighter. But it can only stay that way if we orchestrate AI systems safely using machine identities across the enterprise. There’s no one better equipped to help you do that than Venafi. Our Control Plane for Machine Identities was built from the ground up to help protect your encryption keys and digital certificates, but you don’t have to just take our word for it.
Click the link below to see how one of our global customers secured their systems through centralized, automated machine identity management.
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