The AI adoption rate among large businesses has risen to match burgeoning AI value. IBM reports that 35% of global businesses have adopted AI, with nearly half of these working to embed AI in current applications and processes.
Yet with new threats and hacks reported daily, organizations must safeguard their AI projects or risk losing their investments.
In this blog, we share what you need to know about implementing effective end-to-end AI system cybersecurity.
Data acquisition and learning: Secure the algorithm
All machine learning solutions utilize vast amounts of data to develop their AI algorithms. The data acquisition process is a significant target for cyber attacks seeking to influence the learning process – known as data “poisoning.”
By manipulating data used for learning, attackers introduce biases, contaminate data sets, and shift learning away from the algorithm’s intended trajectory. This can render further development worthless and represents a total loss of investment.
As poisoning targets data before learning starts, these 3 pre-emptive measures can secure the algorithm against contaminated data:
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Analyze and clarify the data required. Ensure your team knows the specific data sets needed for acquisition. This allows them to “scrub” acquired data for irregularities that might harm the learning process.
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Desensitize the learning data. Removing personal or overly sensitive data makes the learning sets less attractive to cyber hackers. You also ensure compliance with data privacy legislations like the GDPR+ and Data Act 2022 and avoid privacy violations that render learning data and any resultant algorithm model unusable.
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Diversify the learning process. Techniques like adversarial learning and randomization can teach algorithms to recognize irregularities, flag them to teams, and purge them from the learning process.
Application-level defenses: Secure the final product
Completed AI algorithms are valuable business assets. Preventing attackers from reproducing your model by manipulating the deployed application is crucial.
Attackers can study an application’s outward-facing responses to learn how its algorithm reaches decisions. They can then reverse-engineer it based on their observations, exposing it to attacks that can bypass its security.
These “inference” attacks target the finished product and require application-level defenses to counter them effectively. You should:
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Train the AI to detect suspicious behavior. You can teach the AI to recognize and flag common attack behaviors, such as a large volume of identical or multiple requests from the same source.
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Filter decisions multiple times. The “defensive distillation” technique runs decisions through two or more models. This identifies erroneous decisions and hinders attackers from mapping the algorithm’s processes.
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Minimize output information. Attackers can vary inputs to discern how the algorithm arrives at results and makes decisions. Minimizing outputs limits information provided, hampering efforts to reverse-engineer the algorithm.
Planning, personnel, and compliance: Secure human resources
Human involvement is the decisive factor in AI system cybersecurity, present at every stage of project development. With airtight data acquisition and solid application defenses, attackers will attempt infiltration via less secure external vectors:
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Trusted MSPs with privileged access to the project
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Weak IAM and perimeter security
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Negligent security practices by personnel
These vectors involve repeated and unsupervised human interactions with the data security architecture. Lost passwords, stolen devices, and phishing breaches provide entry points for infiltration and access. The invasions often go undetected, giving attackers loiter time to map security architecture, install malware, steal or contaminate data, and sabotage the project.
Efficient personnel management, stringent MSP oversight, and tightened data architecture security are the most effective means to reduce the risk of human-related breaches where you:
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Coordinate internal teams. Data scientists are crucial to acquisition and learning but lack cybersecurity expertise – vice versa for cybersecurity teams and data. Stronger coordination between both teams helps identify and eliminate risks before they become threats.
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Tighten vendor oversight. MSPs’ expertise and solutions make them valuable additions to AI projects, but they are also potential entry points. Standardize security practices and MSP privileges to improve cybersecurity system integrity.
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Overhaul data architecture. The most cost-effective approach to centralized security is to overhaul the entire system. Implementing updated protocols like MFA and zero-trust architecture can enhance system-wide IAM, attack detection, and expulsion.
Dedicated cybersecurity expertise can help map your project environment and its unique security risks. This enables you to implement end-to-end cybersecurity for your AI projects. With solutions optimized to your needs, you can advance your AI/ML investments with peace of mind.
Wavestone’s cybersecurity experts can help you secure your AI/ML projects so you can make the most of your AI investments.
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