EXPERT OPINION
Managing , mitigating and measuring risk objectively is the fundamental shift required and this comes with the knowledge of an enterprise ’ s breach likelihood .
subjective abstractions of the CIO , security team or competitor enterprises .
On average , enterprises deploy 45 cybersecurity-related tools . However , there is a definite lack of cohesiveness in determining what is going well and what could be better . To put it into perspective , enterprises that deploy over 50 cybersecurity tools rank themselves 8 % lower in their ability to detect threats than other companies employing fewer toolsets .
There is no industry standard determining the fundamentals enabling Financial Institutes ( FI ) to answer one simple question : How secure are they today ? When the CEO can be held accountable for an organisation ’ s breach
ABOUT THE AUTHOR
( as per the GDPR ), the board gets more curious and involved in the decisionmaking processes of cybersecurity than ever before .
In such a scenario , cybersecurity should transform from being jargon-rich to simple , unified and easy . Managing , mitigating and measuring risk objectively is the fundamental shift required and this comes with the knowledge of an enterprise ’ s breach likelihood .
Financial institutions needed to adopt breach likelihood yesterday
Gartner defines Integrated Risk Management ( IRM ) as ‘ practices and processes supported by a risk-aware culture and enabling technologies , that improve decision-making and performance through an integrated view of how well an organisation manages its unique set of risks ’.
The building block of IRM is enterprise risk . Currently , organisations have tried and failed to protect data by looking at cybersecurity through compliance frameworks only , with point-in-time reports from siloed tools . It is time they moved from reactive and defensive risk management to predictive risk management through breach likelihood , which simplifies cybersecurity .
Computing an enterprise ’ s breach likelihood leverages technology that is not alien to the BFSI sector . Machine
Saket Modi is the Co-founder and CEO of Safe Security , a Cybersecurity and Digital Business Risk Quantification platform company . A computer science engineer by education , he founded Safe Security in 2012 while in his final year of engineering . Incubated in IIT Bombay and backed by Cisco ’ s former Chairman and CEO , John Chambers , Safe Security protects the digital infrastructure of multiple Fortune 500 companies around the world with its cyber-risk measurement and mitigation platform called SAFE . Modi is a part of Fortune Magazine ’ s 40-under-40 , Entrepreneur Magazine ’ s 35-under-35 , Forbes Magazine ’ s 30-under-30 lists , among others .
Learning-enabled predictions are already being deployed in insurance , employee welfare and customer experience .
A large online payments system uses Deep Learning , algorithms , multi-class models and more to sieve fraudulent and genuine transactions by deriving actionable insights from its story-model analysis . Cybersecurity can also be simplified using technology that already exists . The fundamental element of cybersecurity is as basic as knowing the enterprise breach likelihood that can be calculated from enterprise-wide signals .
Breach likelihood prediction in the banking sector shifts power to the cybersecurity team and the organisation , enabling them to prevent rather than react to threats . Be it the possibility of a breach through ransomware , cloud
42 www . intelligentciso . com