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Cyber resilience has made it to the top of the priority list as banks face off against increasingly sophisticated digital threats. Talent is the remedy, but perpetual skill gaps make it tough to build defensible functions.
‘Working on AR and VR was like the Wild West before regulations were introduced,’ a CISO told us at a recent roundtable event. ‘Where the technology is way ahead of the regulations and government, it’s a catch-up game.’
The sentiment is fitting for today’s AI landscape. Innovation is racing ahead, and governance is struggling to keep up. One of the major challenges is that the pressure falls on risk and security teams to bridge that gap, often without the specialist talent they need.
AI and model resilience are firmly in the spotlight as firms rush to deploy machine learning into areas like fraud detection and transaction monitoring, although it’s those same systems that introduce novel risks.
Regulators are watching closely. What does this mean for model risk management, and what are its implications on the talent market?
The Impact
According to our LinkedIn data, Banking’s cyber security talent pool has increased by 89% in the last 12 months, with Security Risk emerging as the fastest-growing skill at an incredible 786% growth rate.
We’re seeing the candidate pipeline stretched thin, and the existing talent is feeling the burnout. A report from Hack the Box found that 84% of cybersecurity professionals are experiencing burnout.
That said, investment is expected to grow this year (by as much as 12% according to some reports), and while a good chunk of this looks set to go to tech, we can expect this to drive demand for increasingly specialised candidates.
- Boardroom dynamics are often tested by a lack of technical understanding at the decision-making level. When language barriers get in the way of the urgency that security demands, CISOs, analysts, and engineers need to be storytellers.
- Cyber teams, despite finding a seat at the table in recent years, are made to do more with less, which includes embedding governance obligations into opaque AI systems.
- Specialist skills in cloud and threat intelligence are typically concentrated in small talent pools with strong demand and high churn. According to our LinkedIn data, the average tenure for Security Engineering talent with these skills has shrunk to two years.
- AI-native risk frameworks are evolving, but many firms are still relying on legacy governance models (that don’t work for black box systems). Firms are struggling to find candidates with experience in both worlds.
- The ability to effectively engage with passive talent will be a key differentiator in the race for top talent.
- Demand for data talent is rising, with many ‘traditional’ analytics roles now skewing toward governance, lineage, and AI readiness.
Looking Ahead
When projects get infinitely more complicated, job descriptions tend to do the same. It’s tough to know what to watch out for on both sides of the coin – what should you watch out for as a job seeker? Who should you target as an employer?
Highly mobile cybersecurity candidates (the kind everyone is searching for) are typically rare and expensive. They’re also aware of their value and wise to bloated role scopes, vague reporting lines and outdated tech stacks.
A measured, benchmarked approach to recruitment is the only sustainable way forward.
The same applies here. As cyber risk becomes more embedded in business strategy, we’re helping organisations get clearer about what they need on the talent front.
As always, the team are here to support your unique recruitment needs. Whether it’s data, risk, machine learning, or all of the above, drop us a message to learn more about our staffing and advisory services:
Francis Alexander: Senior Principal Data Consultant at Trust in SODA
Francis.alexander@trustinsoda.com
Connor Nurse: Head of US, Risk, Compliance & Finance Specialist at Broadgate
James Davis: Principal AI/ML Consultant at DeepRec.ai