Cyber Risk

Intelligent Cyber Defense

Machine learning programs are taking cyber security to the next level, though they won't keep hackers at bay for long.
By: | April 9, 2018 • 5 min read

Hailed by some as a Holy Grail for cyber security protection, machine learning programs are helping businesses identify and counter cyberattacks more effectively than ever before.

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From programs scanning the dark web for clues of cyberattacks to software analyzing companies’ data network flows and user behavior, risk managers, CTOs and CIOs have a growing choice of tools at their disposal.

As more devices come online and more data is produced, the potential vulnerabilities hackers can exploit grow exponentially. So too grows the need for tools that help firms spot threats and strengthen their cyber networks.

“It is increasingly important to develop tools to sift through the noise, identify signals and check for anomalies to identify attack vectors that are susceptible and ‘being exploited,” said Eric Cernak, vice president, Hartford Steam Boiler. “Machine learning can really help this process.”

Leveraging powerful algorithms, programs that harness machine learning are getting better at spotting the difference between genuine threats and innocent anomalies. They can detect threats faster than before.

Eric Cernak, vice president, Hartford Steam Boiler

Ryan Griffin, senior vice president, JLT Specialty USA, pointed out that the average time to detect an event was 180 days. Now it can be done in three.

According to ethical hacker and cyber security expert Mike Peters, vice president for IT, RIMS, more than 70 percent of attacks exploited known vulnerabilities in available patches last year. Machine learning programs can process vast quantities of data while iteratively learning, promising to help uncover many of these threats with minimal human involvement, he said.

“Machine learning systems have a big role to play in conquering threats currently handled in a manual fashion.”

“Machine learning systems have a big role to play in conquering threats currently handled in a manual fashion.” – Ryan Griffin, senior vice president, JLT Specialty USA

Credit rating firm FICO launched its own cyber vulnerability assessment tool, which scans the entire web on a weekly basis, gathering data company internet footprints. It learns about the conditional attributes and vulnerabilities exhibited by companies in the lead-up to a breach. Companies using this software scan and compare their own internet footprints to assess their cyber risk level.

According to Graeme Newman, cyber leader, Barbican Insurance, the program has so far found the most at-risk company to be 24 times more likely to suffer a cyber breach than the one with the best risk rating. It also confirmed the belief that certain sectors are more vulnerable than others; budget-strapped education entities typically rated poorly while banks scored highly due to heavy investment in security infrastructure.

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“Hackers have various tools available to them and are looking for certain vulnerabilities. The scanning software gives the company the same view of its internet footprint as any hacker would get,” explained Newman.

“A company that takes the findings very seriously may well look at every individual asset and negative signal with a view to fixing things and putting in new policies and procedures. That’s where the big work comes in.”

Insurance Partnerships

Insurers are also partnering with tech firms to offer similar risk assessment solutions. Allianz recently entered a heavyweight partnership with Aon, Apple and Cisco, harnessing the tech firm’s intelligent Cisco Ransomware Defense software.

Jenny Soubra, head of cyber and tech, Allianz Global Corporate & Specialty

Jenny Soubra, head of cyber and tech, Allianz Global Corporate & Specialty, claimed Cisco offers the most holistic cyber security solution in the market, and its counterintelligence measures identified the WannaCry virus a month ahead of the attack, allowing it to warn and protect its customers.

“Different levels of coverage are unlocked by different levels of software and hardware deployment,” Soubra explained, revealing that the insurer is offering users “broad terms and conditions not currently available in the marketplace,” with deductibles in some cases reduced to zero.

Over time, this kind of arrangement is likely to become more prevalent, and machine learning tools will have a big impact on cyber insurance terms and pricing — not only by reducing the risk of insureds suffering breaches but also generating invaluable data to help refine underwriting.

One key challenge: risk aggregation. These tools could help identify vulnerabilities within supply chains, allowing users to suggest security improvements to suppliers and clients.

Costs and Considerations

Uptake is limited. Asked if machine learning is being translated into meaningful risk mitigation, Griffin said, “We haven’t seen that yet on the client side. As an industry, we’re kidding ourselves that we’re going to change the behavior of complex insureds who have invested millions of dollars into an infrastructure and a security team.”

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Costs, which vary significantly, may be prohibitive to smaller organizations.

“These programs can be expensive,” said Peters, noting annual prices on a per-user or per-instance basis can range from $1,000 to $20,000 and upwards. “It’s not for the faint of heart.” And while the range of machine learning tools continues to grow and improve, they do not offer a silver-bullet solution: “These platforms are only as good as the person administering them,” Peters said.

“It takes a lot of time and effort to fine-tune them to fit your business needs and day-to-day processes. Increasing network traffic may be statistically interesting, but it rarely represents an attack, and systems that look for generic anomalies can often misclassify a threat. You have to know how to apply machine learning in order for it to reveal true insight,” he added.

Newman agrees that false positive or negative signals are possible, such as spotting vulnerabilities in assets that may be disconnected from key services or are of little value or importance. Barbican’s approach, he said, is to use the signals as the starting point for risk mitigation conversations.

Ryan Griffin, senior vice president, JLT Specialty USA

Machine learning is still young, and these tools will only become more accurate and effective. However, warned Griffin, “as technology evolves, so does the threat.”

Sophisticated cyber criminals will soon harness the power of machine learning to make their attacks more effective. This could make smaller firms without intelligent security systems particularly vulnerable — not only for the assets they possess but also as routes into larger organizations.

Cernak hopes machine learning programs will become easier to deploy, improving smaller companies’ access to these powerful tools. However, even the adoption of high-end software is pointless if the human principles of cyber security are not adhered to. Adequately training staff remains vital as human error is almost always present in the event of a breach.

“You have to treat machine learning like any other tool in your toolbox. You can’t become overly dependent on any one solution,” said Cernak. “It’s important to build a culture of risk awareness and prevention. Cyber security starts and ends with people, and the tools you deploy need to be complementary to that strategy.” &

Antony Ireland is a London-based financial journalist. He can be reached at [email protected]

More from Risk & Insurance

More from Risk & Insurance

4 Companies That Rocked It by Treating Injured Workers as Equals; Not Adversaries

The 2018 Teddy Award winners built their programs around people, not claims, and offer proof that a worker-centric approach is a smarter way to operate.
By: | October 30, 2018 • 3 min read

Across the workers’ compensation industry, the concept of a worker advocacy model has been around for a while, but has only seen notable adoption in recent years.

Even among those not adopting a formal advocacy approach, mindsets are shifting. Formerly claims-centric programs are becoming worker-centric and it’s a win all around: better outcomes; greater productivity; safer, healthier employees and a stronger bottom line.

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That’s what you’ll see in this month’s issue of Risk & Insurance® when you read the profiles of the four recipients of the 2018 Theodore Roosevelt Workers’ Compensation and Disability Management Award, sponsored by PMA Companies. These four programs put workers front and center in everything they do.

“We were focused on building up a program with an eye on our partner experience. Cost was at the bottom of the list. Doing a better job by our partners was at the top,” said Steve Legg, director of risk management for Starbucks.

Starbucks put claims reporting in the hands of its partners, an exemplary act of trust. The coffee company also put itself in workers’ shoes to identify and remove points of friction.

That led to a call center run by Starbucks’ TPA and a dedicated telephonic case management team so that partners can speak to a live person without the frustration of ‘phone tag’ and unanswered questions.

“We were focused on building up a program with an eye on our partner experience. Cost was at the bottom of the list. Doing a better job by our partners was at the top.” — Steve Legg, director of risk management, Starbucks

Starbucks also implemented direct deposit for lost-time pay, eliminating stressful wait times for injured partners, and allowing them to focus on healing.

For Starbucks, as for all of the 2018 Teddy Award winners, the approach is netting measurable results. With higher partner satisfaction, it has seen a 50 percent decrease in litigation.

Teddy winner Main Line Health (MLH) adopted worker advocacy in a way that goes far beyond claims.

Employees who identify and report safety hazards can take credit for their actions by sending out a formal “Employee Safety Message” to nearly 11,000 mailboxes across the organization.

“The recognition is pretty cool,” said Steve Besack, system director, claims management and workers’ compensation for the health system.

MLH also takes a non-adversarial approach to workers with repeat injuries, seeing them as a resource for identifying areas of improvement.

“When you look at ‘repeat offenders’ in an unconventional way, they’re a great asset to the program, not a liability,” said Mike Miller, manager, workers’ compensation and employee safety for MLH.

Teddy winner Monmouth County, N.J. utilizes high-tech motion capture technology to reduce the chance of placing new hires in jobs that are likely to hurt them.

Monmouth County also adopted numerous wellness initiatives that help workers manage their weight and improve their wellbeing overall.

“You should see the looks on their faces when their cholesterol is down, they’ve lost weight and their blood sugar is better. We’ve had people lose 30 and 40 pounds,” said William McGuane, the county’s manager of benefits and workers’ compensation.

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Do these sound like minor program elements? The math says otherwise: Claims severity has plunged from $5.5 million in 2009 to $1.3 million in 2017.

At the University of Pennsylvania, putting workers first means getting out from behind the desk and finding out what each one of them is tasked with, day in, day out — and looking for ways to make each of those tasks safer.

Regular observations across the sprawling campus have resulted in a phenomenal number of process and equipment changes that seem simple on their own, but in combination have created a substantially safer, healthier campus and improved employee morale.

UPenn’s workers’ comp costs, in the seven-digit figures in 2009, have been virtually cut in half.

Risk & Insurance® is proud to honor the work of these four organizations. We hope their stories inspire other organizations to be true partners with the employees they depend on. &

Michelle Kerr is associate editor of Risk & Insurance. She can be reached at [email protected]