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

2018 Risk All Stars

Stop Mitigating Risk. Start Conquering It Like These 2018 Risk All Stars

The concept of risk mastery and ownership, as displayed by the 2018 Risk All Stars, includes not simply seeking to control outcomes but taking full responsibility for them.
By: | September 14, 2018 • 3 min read

People talk a lot about how risk managers can get a seat at the table. The discussion implies that the risk manager is an outsider, striving to get the ear or the attention of an insider, the CEO or CFO.

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But there are risk managers who go about things in a different way. And the 2018 Risk All Stars are prime examples of that.

These risk managers put in gear their passion, creativity and perseverance to become masters of a situation, pushing aside any notion that they are anything other than key players.

Goodyear’s Craig Melnick had only been with the global tire maker a few months when Hurricane Harvey dumped a record amount of rainfall on Houston.

Brilliant communication between Melnick and his new teammates gave him timely and valuable updates on the condition of manufacturing locations. Melnick remained in Akron, mastering the situation by moving inventory out of the storm’s path and making sure remediation crews were lined up ahead of time to give Goodyear its best leg up once the storm passed and the flood waters receded.

Goodyear’s resiliency in the face of the storm gave it credibility when it went to the insurance markets later that year for renewals. And here is where we hear a key phrase, produced by Kevin Garvey, one of Goodyear’s brokers at Aon.

“The markets always appreciate a risk manager who demonstrates ownership,” Garvey said, in what may be something of an understatement.

These risk managers put in gear their passion, creativity and perseverance to become masters of a situation, pushing aside any notion that they are anything other than key players.

Dianne Howard, a 2018 Risk All Star and the director of benefits and risk management for the Palm Beach County School District, achieved ownership of $50 million in property storm exposures for the district.

With FEMA saying it wouldn’t pay again for district storm losses it had already paid for, Howard went to the London markets and was successful in getting coverage. She also hammered out a deal in London that would partially reimburse the district if it suffered a mass shooting and needed to demolish a building, like what happened at Sandy Hook in Connecticut.

2018 Risk All Star Jim Cunningham was well-versed enough to know what traditional risk management theories would say when hospitality workers were suffering too many kitchen cuts. “Put a cut-prevention plan in place,” is the traditional wisdom.

But Cunningham, the vice president of risk management for the gaming company Pinnacle Entertainment, wasn’t satisfied with what looked to him like a Band-Aid approach.

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Instead, he used predictive analytics, depending on his own team to assemble company-specific data, to determine which safety measures should be used company wide. The result? Claims frequency at the company dropped 60 percent in the first year of his program.

Alumine Bellone, a 2018 Risk All Star and the vice president of risk management for Ardent Health Services, faced an overwhelming task: Create a uniform risk management program when her hospital group grew from 14 hospitals in three states to 31 hospitals in seven.

Bellone owned the situation by visiting each facility right before the acquisition and again right after, to make sure each caregiving population was ready to integrate into a standardized risk management system.

After consolidating insurance policies, Bellone achieved $893,000 in synergies.

In each of these cases, and in more on the following pages, we see examples of risk managers who weren’t just knocking on the door; they were owning the room. &

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Risk All Stars stand out from their peers by overcoming challenges through exceptional problem solving, creativity, clarity of vision and passion.

See the complete list of 2018 Risk All Stars.

Dan Reynolds is editor-in-chief of Risk & Insurance. He can be reached at [email protected]