As business leaders, we’ve given lots of thought to the pitfalls and possibilities of implementing AI. For every use case we see in healthcare, insurance, or banking, we weigh the potential risks of artificial intelligence in business settings. With all innovations, there are certain precautions that must be taken. AI TRiSM can be helpful in this regard, providing a primary guardrail to keep innovation on track.
AI TRiSM—which stands for trust, risk, and security management—has the potential to not only protect the systems your business is implementing but prevent AI-powered threats from hurting your infrastructure, insights, or reputation. Here’s an overview of how this holistic approach can help your business maintain effectiveness, trust, and security across your IT and operations.
What Is AI TRiSM and Why Should You Apply It?
In a nutshell, AI TRiSM is an overhaul of your risk management mindset. Organizations that embrace this approach can cultivate a culture of proactive risk assessment that identifies, evaluates, and mitigates the shortcomings and vulnerabilities of AI systems.
What types of risks can this approach identify? Let’s start with cyber threats. It’s long been predicted that, like any innovation, hackers will misuse artificial intelligence to increase their success rates. We’re likely already seeing that in action. Algorithms can already cycle through character combinations at breakneck speeds, expediting the results of brute force attacks.
We’re also getting near the point of self-sustaining malware, where computer viruses can evolve like their biological namesake to proactively evade or even overcome the defense measures and best practices of cybersecurity professionals. Only AI on your side can combat these threats.
There is also a serious risk of artificial intelligence systems being compromised. Using coopted credentials, hackers can insert bad or misleading data into training models, skewing your analytical results for their own agenda, biases, or desire for chaos. With corrupted data at the root of your insights, your business can suffer from a cascading series of mistakes and false assumptions that can have a negative impact on the vision, performance, ethics, and profits of your organization.
How to Use the Pillars of AI TRiSM
Implementing TRiSM frameworks can combat these issues. However, many organizations are either too slow in implementing these strategies or don’t know where to start. Fortunately, AI TRiSM provides several pillars that can simplify the journey into total preemptive risk management.
Explainability
Do your decision makers understand the “rationale” behind AI decisions? If not, there’s a risk the outcomes can be corrupted, flawed, or outright wrong. Sadly, there is a tendency for organizations to treat AI like an impenetrable and indecipherable black box, which makes leadership less likely to analyze or reflect upon these findings. Such negligence opens the door for bad or malicious data to pollute outcomes and analysis long before stakeholders catch it.
Explainable AI within the AI TRiSM methodology aims to shine a light onto the underlying computations of these algorithms and programs. Organizations can determine prediction accuracy by evaluating the results of multiple simulations of the original training data or appraising classifiers within machine learning algorithms.
Also, they can establish traceability by thoroughly defining and narrowing the rules and features that AI must abide by. In short, creating the ability to double check and clarify results at every stage and level of AI actions can instill the confidence your leaders need to accept, revise, or reject AI-powered recommendations.
ModelOps
How do we turn the abstract idea of AI risk management into something measurable and concrete? Under the umbrella of AI TRiSM, ModelOps is geared towards operationalizing risk management throughout the AI platform development lifecycle.
This end-to-end approach, much like DevOps, prioritizes security, compliance, and scalability at every step of the process. That means creating communication between AI engineers, data scientists, analysts, and end users so there is ongoing testing and monitoring as artificial intelligence initiatives move towards deployment.
As with other “Ops” methodologies, there’s also a push to automate any repetitive or redundant functions to eliminate human error in the process. Coming back to the holistic approach, this level of automation can only work if there is a clear model of explainability in place.
There’s good news with this pillar: Gartner reports that 25% of respondents in a recent survey are currently using or implementing ModelOps, showing more companies are moving towards comprehensive risk management.
Data Anomaly Detection
This AI TRiSM strategy is vital for data hygiene, cybersecurity, and analysis. Data anomaly detection is used to identify instances where data points deviate significantly from the norm, which, when it comes to artificial intelligence, is nonnegotiable. Again, we need to understand whether an outlier in data is a natural occurrence, an issue with measurement, a problem with labelling, or a malicious manipulation (all instances that point back to explainability).
The trick is having firm understanding and control of the training data before letting the model go live. First, your data analysts need to review all the data inputted into the training model to identify any biases, faults, or limitations within the foundational data sets. Once in use, the AI tool can and should be compared against the training model to identify any potential data drift. This maintains integrity of the analysis and the high performance for the intended purpose.
Adversarial Attack Resistance
How attuned is your business to the way outside influences can compromise your AI models? That is the goal of building a strong adversarial attack resistance strategy. Attackers can use either a comprehensive understanding of the model to insert calibrated yet malicious data examples or probe the system to find the right set. Whichever approach they take, your organization needs to be prepared.
Adversarial attack resistance incorporates several techniques that can proactively prepare AI systems to ward off these intrusions. For instance, participating in adversarial training, where you prepare AI training models to detect and classify malicious inputs, can help the finalized AI model quarantine and notate malicious inputs.
Additionally, organizations can mask information about deep learning neural networks (e.g., their gradient) to prevent attackers from learning how to effectively create plausible (yet false) inputs. Going on the offense with this element of AI TRiSM allows the other frameworks to detect less malicious issues and nip them in the bud.
Building the Foundation for AI TRiSM
At the end of the day, explainability, ModelOps, data anomaly detection, and adversarial attack resistance are only part of a larger picture. Organizations across industries need to take a holistic approach to create ethical, effective, and secure implementation of AI tools and platforms. Sometimes, that means getting back to basics.
The underlying data of any AI system needs to adhere to proper data management and governance. Creating a trustworthy data pipeline built on quality, timeliness, and accuracy will maximize the ability of AI TRiSM methodology to find biases and flaws in the system without worrying about whether you can trust the data. When you can trust the oceans of data flowing into your organization, you can unlock a world of insights and capabilities that would have been sealed away just under the surface.
Are you looking to keep up with AI TRiSM and other governance theories? Follow the w3r Consulting blog to stay in touch with the latest innovation.
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