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by Aware
Every tool in your organization creates large amounts of data on a daily basis, and all that data needs to be secured against insider risks and potential security breaches. But simply getting your arms around this unstructured data set can be a colossal task, leaving gaps through which threat actors can exfiltrate valuable information undetected. AI supports risk mitigation in organizational data sets by ingesting and analyzing huge amounts of information in real time, detecting more risks and reducing false positives common with traditional, rule-based monitoring tools.
In this article, we’ll review the current use cases for AI in risk management, as well as challenges, processes, and best practices. Read on to discover how you can harness the power of AI to protect your company data today.
Contents
- How is AI used for risk management?
- How does AI improve the risk management process?
- Challenges of AI for risk management
- Best practices for incorporating AI in risk management
- Partner with Aware to harness AI for risk management
How is AI used for risk management?
AI’s learning capabilities make it well suited for risk detection and other cybersecurity measures in large data sets. It can spot patterns in behavior and anomalous activity in real time, helping organizations to reduce the risks associated with negligent or malicious data handling.
Additionally, AI algorithms can be used to detect unique information, such as an organization’s intellectual property, that isn’t searchable by regex or keyword.
How does AI improve the risk management process?
AI supports risk management large volumes of data through two primary capabilities—it’s vast computing power, and its ability to learn and improve over time. Within single collaboration tool such as Slack, just 100 employees can generate over 34,000 messages per month. That data contains an incredible amount of risk—credit card numbers, SSNs, code, passwords and other access credentials, and more. AI can ingest those messages in real time, analyze them for potential risks, and quickly mitigate any unauthorized behavior they contain.
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Employees are wise to the limitations of legacy tools, which work by detecting regular expression and keywords. They know to break up credit card numbers, obfuscate passwords, and use coded language to fool algorithms and avoid detection.
In fact, Aware research, based on analysis of over 6 billion real collaboration messages, discovered that employees often use images or screenshots to share sensitive data. However, in tools where message attachments and images can be easily filtered in search results, these attempts at security may make the data more discoverable, not less. AI models trained on these data sets can spot this behavior and correctly identify the risk by analyzing the messages in context. This leads to enhanced fraud detection and fewer false negatives compared with traditional DLP tools.
Artificial intelligence also supports use cases beyond data protection. For example, AI can provide real-time compliance automation for regulatory bodies and internal acceptable use policies alike. Whether to support the GDPR or CCPA/CPRA, to comply with HIPAA or SEC 17a-4, or to prevent employees from sharing code in unrestricted channels, AI can help enforce the rules and keep the organization compliant.
Uncover the true scale of risk in your collaboration data.
Aware's data scientists analyzed 6.6 billion real messages so you can understand what lives in your data.
Challenges of AI for risk management
AI systems are not a monolith, and not all AI models are suitable for detecting risk in business data sets. If an organization uses models that have been trained on generic data, they can easily miss more specific risks. Equally, poorly trained models can introduce bias that reinforces its own mistakes and delivers even more incorrect results over time, not less.
There may also be legal liabilities to consider. When you integrate AI/ML models with your data, what happens to it? Is your data used for training, and could it ever become accessible to users outside your organization? Privacy concerns surrounding generative AI have made headlines in recent years, but this is not the only type of AI technology on the market.
When considering any AI purchase, corporate buyers should understand the types of models they’re purchasing, how they were trained, how they will continue to evolve and refresh, and what kind of infrastructure they will need to support them as they grow. Often, the best AI for the job is small, closely trained models that are highly efficient, quick to refresh, and cost-effective to run.
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Best practices for incorporating AI in risk management
While it seems every organization is rushing to adopt AI, it makes sense to pause and consider how to make AI work for your organization. These best practices will put you on the right track.
Understand your use case(s)
It’s important to spend your AI dollars on models that serve a purpose by conducting a thorough risk assessment. A recent survey found that AI purchases increased the workload of 77% of employees, often because they are using generic, inefficient models and expecting precise results.
Identify your data sources
Perform a review of all the tools and programs in use across the company where data is created to identify vulnerabilities. Consider the presence of shadow IT alongside sanctioned products, and have a plan to integrate unauthorized tools or transition employees away from them. Ensure that the AI you are vetting is compatible with the data to be analyzed.
Recruit other stakeholders
AI adoption comes with risks that legal, regulatory compliance, and security teams will want to review. Get ahead of challenges by proactively involving these stakeholders in the decision-making process early to understand their requirements for your AI initiatives and make informed decisions.
Make the AI fit your workflow
For the greatest chance of full adoption, your new AI tools should meet you where you’re already at. That means seamless integration with your data sources ecosystem, low/no IT lift or maintenance, and alerts and reports that suit your risk management framework.
Establish oversight and governance
Define an oversight and governance framework to validate the effectiveness of the AI risk management plan and ensure compliance with relevant regulations, such as the EU’s Artificial Intelligence Act. Some organizations may find they have more obligations than others, particularly those in regulated industries like financial institutions, financial services, healthcare providers, and government contractors.
Provide a continuous feedback loop
For your AI to continue to be effective, it must be trained and refreshed. Model inference (refresh) rates will depend on the size and scale of the model and the complexity of the data being analyzed. Balance cost vs performance and ensure you have proactive ways to offer feedback on results that improve the model’s outputs.
Ethical AI considerations
Incorporate ethical considerations into AI governance to address issues such as bias, fairness, and transparency. This includes educating and upskilling the workforce on AI risk management to further organizational resiliency. Perform routine audits to ensure you are leveraging AI appropriately.
Partner with Aware to harness AI for risk management
Aware’s proprietary AI/Machine Learning models were designed and trained on collaboration tool data from platforms like Slack, Teams, and Zoom to identify and mitigate risks in real time.
- Hand-labeled data for greater accuracy and fewer false positives
- Industry-leading natural language processing that outperforms Google, Microsoft, and Meta
- Designed for enterprise scale, and backed by enterprise-grade security
- API and webhook integrations mean zero IT lift or end-user impact
Using Aware, organizations unlock fast, federated search across their entire collaboration stack from a secure, central platform. Integrate with your existing security, compliance, and legal workflows and coach employees on acceptable use in real time. Request a demo to learn more about how Aware’s AI-powered insights can transform your risk management strategy.
Learn more about how to protect your data with Aware. Request a demo today.
