Team discussing cybersecurity strategy in front of laptops

Can Your Cybersecurity Strategy Stop a Determined Attacker?

We all have a door in our home that lets in a draft. While all doors hang on hinges and essentially function the same, it’s not guaranteed a specific door in your home is perfectly fit to completely protect you from the elements.

The same principle applies to your business’ cybersecurity strategy. Companies have long relied on device intelligence data (including a device’s OS type, IP address and time zone) as their “front doors,” or the primary means of differentiating between trusted and risky online users. But as the number of internet-connected devices globally rises to hit 29 billion this year, there’s growing urgency around introducing additional security layers that aid in the verification process.

Historically, device ID has proven effective in recognizing both trusted and risky devices. And collecting device intelligence remains a crucial step in any proactive and multi-layered cybersecurity strategy — it’s just no longer enough on its own to halt determined attackers armed with every trick in the book.

Think of today’s fraudsters like a mischievous fox. Some fraudsters are more sly and slick than others, but your cybersecurity strategy should be able to prevent even the toughest challengers. In 2022, that includes addressing the gaps in device-based data as a result of increased privacy regulations as well as navigating highly effective spoofing methods that now make it easier for fraudsters to thwart common device intelligence-based protections.

What threats does your front door keep out — and which slip through?

By lending to the creation of robust user data, device intelligence improves your fraud-detection capabilities while also strengthening your user experience. Device intelligence assesses a user’s unique device characteristics, such as a device’s operating system, browser and browser version, IP address, and more. This data is combined through algorithms to create unique identifiers for every customer’s device, which include device IDs, device fingerprints and device user-agents.

Armed with this information, it’s possible to detect risk from suspicious devices, including fraudsters who attempt to guess our passwords and replicate other common login credentials. However, while at one point very useful on its own, device intelligence can no longer alone capture the complete picture.

Today’s fraudsters can easily mask, disguise, and tamper with our devices, circumventing popular intelligence-based restrictions and roadblocks. That means a large swath of bad actors are now well-versed in common lock-picking strategies such as creating double breaks on devices so common device-based tools can’t recognize them as risky. This makes it all too easy to crack open your front door — and gain access to every personal piece of information stored within.

Specifically, fraudsters can now turn to developer tools that make small adjustments to devices and emulate characteristics of trusted devices. Bad actors can alter many aspects of an attack like screen resolution, operating system, browser, and even IP address with minimal time and effort, which makes it more difficult for companies to determine which devices exhibit risky traits — and therefore require intervention — and which are simply good users going about their business.

While in many cases your existing front door works well as a deterrent, some situations call for additional layers of security. To finish off our metaphor, that could be a robust surveillance system powered by cameras and lasers. These tools enhance security, but don’t complicate processes. When it comes to cybersecurity, consider adding in behavioral tools.

3 steps to achieve a multi-layered cybersecurity strategy

If you’re already using device intelligence to identify trusted users, you have a solid foundation for integrating additional anti-fraud technologies. Consider these three steps to build a multi-layered cybersecurity strategy that thwarts even the most determined attackers.

1. Invest in a long-term solution

Companies don’t need every security solution, they need the right combination for their environment. For a beach home in the tropics, a screen door may be enough to keep out the mosquitoes. In a colder environment, your door needs to be heftier.

It may seem like a big undertaking to invest in multiple anti-fraud solutions beyond the device-based tools you already own, especially if you’re a smaller organization. But a multi-layered cybersecurity approach is likely your best long-term tactic to proactively mitigate fraud and avoid costly oversights down the line.

When building out your organization’s cybersecurity roadmap, factor in the need to invest in both required talent and resources to implement additional anti-fraud intelligence and technologies — or find a partner adept at connecting your business with solutions that enable safe, secure and seamless user experiences. The more secure your digital user touchpoints are, the stronger your resulting user experiences. A partner that supports you in more quickly engineering a trusted environment can prove crucial — you can’t expect to earn long-term customer loyalty and repeat business if your experiences are clunky, risky or both.

2. Combine device intelligence with behavioral technologies

Introducing passive biometrics and behavioral analytics deepens the insights you gain from your customers, enhancing users’ digital profile with device interaction intelligence (e.g., how users hold devices or what time of day they generally log on).

The unique, nuanced information surfaced through behavioral strategies is harder for fraudsters to imitate. Used in tandem with device intelligence, behavioral information paints a more complete picture of each user’s profile and makes it easier to weed out fraudsters deviating from known customer behaviors and patterns. It’s like giving trusted end users a key to open your door when they need to.

3. Match your security response to your company’s risk tolerance

When layered together, device and behavioral intelligence allow you to more fully customize your fraud interventions and factor in the unique digital experiences customers in your industry expect. If your industry has a low tolerance for risk, for example, you may task your security solution with providing individual user risk scores for each customer, and even request human intervention whenever a user’s score falls below a predetermined threshold. Intentionally introducing friction leaves good users’ experiences with your business uncompromised and their trust in your ongoing security strategies high — and fraudsters without a way to get in.

Conversely, if your professional world has a high tolerance for risk, you might benefit from an anti-fraud strategy that lets more occurrences of low-risk behavioral deviations pass through. This means you’re less likely to introduce unnecessary friction into your user experience, which often benefits those in industries where customers engage often and at lower-dollar amounts.

Of course, even in environments with traditionally lower levels of risk, context is always key. What experiences do your users expect for a given account? What is the potential threat? For example, a user may be more receptive to complete multi-factor authentication for an account holding their life savings, but less so for their online streaming services.

The best approach is ultimately to tailor your intervention strategies to align with your company’s risk tolerance and customers’ expectations.

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If your current system isn’t capable of tracking and assessing different user variables online, chances are good your business isn’t fully prepared to stop a determined attacker. What layers is your cybersecurity strategy missing?