Editor’s Note: Here at Cyber Oregon, we are fortunate to have some of the top technology companies not only in Oregon but from across the industry as sponsors. This means we can tap into their knowledge and wisdom on cybersecurity topics that matter to Oregonians on a regular basis, including some already on our Resources page. From time to time, we will be sharing content from experts at our sponsor organizations on this blog. Below is a timely post authored by Otto Berkes, Chief Technology Officer at CA Technologies. In the post, Berkes predicts that when it comes to cybersecurity, “AI will pose new threats that the enterprise will have to cope with, and fighting AI with AI will become a necessity.” Here come the drone wars! You can find the original post here.
Every year about this time, we gaze into crystal balls to divine the future of our industry – or at least where it’s headed over the next 365 days. The result is often a triumph of incrementalism: we predict that we will get more of what we already have. The truth is, technology isn’t as revolutionary as we often think – and commenting on incremental changes alone may not help us understand what lies ahead.
Along with a few near-term predictions – so hard to resist – I’d also like to make some predictions not just about technology per se, but about related changes to organizations, processes, and the cultures around them. Here’s my main prediction: By 2030 what we’ve come to know as “IT” today will be virtually unrecognizable.
No-code software will drive truly distributed technology
By 2030, the “de-codification of coding” – meaning the use of no-code or low-code platforms – will become real. Assembling code blocks into new applications will be possible without having to manipulate the underlying code itself. And, software that “learns to learn” will deliver on the dream of self-writing software that evolves itself through learning.
Distributed technology will accelerate democratization of innovation
The center of gravity and decision-making is shifting away from top-down IT bureaucracy to bottoms-up adoption. Monolithic, centralized applications will give way to distributed and agile solution development. Technology-driven innovation will be able to come from anyone, anywhere, not just dedicated technology organizations.
The ecosystems of tomorrow will be a hybrid of people and machines
Technology tomorrow will be an ecosystem of people and machines. These horizontal ecosystems of technology and people will serve both existing sectors and emerging business models – spinning up and down in real time to drive competitive advantage.
These three factors will be part of a future where technology becomes the primary enabler for individuals to achieve business goals, and will put pressure on the traditional enterprise structure. The “gig economy” is only the first expression of this nascent way to organize businesses, and even, perhaps, entire economies and nation-states.
If all that is the shape of the longer-term future, then what are the trends that we will see amplified in the next year? I outlined some of the major areas in a recent keynote. In short:
Data and analytics will start to revolutionize Agile as we know it. As continuous delivery models expand and accelerate, Agile will have an increasing appetite for data-driven insights. Data insights become integrated into an increasingly granular and fast-paced process for creating new value. This will all be driven by sophisticated insight-generating engines tied to real-time business and financial metrics.
Bottom Line: The health and vitality of your software experiences and investments will be measured and even predicted in ways not possible ever before.
Automation happens today through things like continuous test, release, and business process automation. But to truly capitalize on automation, you need to standardize and integrate workflows smoothly across the DevOps process and tool chain. Analytics will help find bottlenecks or weak spots in your automated software flows.
Bottom Line: The future of automation is intelligent – it learns, adapts, and self-optimizes the entire system – and we will increasingly see software that is running development alongside and, in certain instances, instead of humans.
This is the year that we will start getting better at what we mean by AI. It’s not about sentient robots – yet, anyway – but in essence is a set of algorithms expressed as code operating on data. Advanced analytics engines are the “tip of the spear” of AI across the software development lifecycle. AI and machine learning are driving a fundamentally different approach to software development. Machine intelligence will finally deliver on the promise of big data, and the power of learning-based systems will help build and deliver better software faster.
Bottom Line: The core activities of managing, governing, and securing your technology won’t go away, but they will become more efficient, automated, and intelligent. And this will help you focus more of your energy one what matters: building new value to drive your business forward.
As software becomes the primary way that customers interact with brands, security is becoming synonymous with “trust”. This means you are now securing the entire value chain of your company – including your brand – not just data itself.
Our ability to reduce threat vectors by both enhancing the intelligence of identity and driving more sophisticated analytics will improve, but so will hackers’ ability as machine learning and AI become part of the security threat landscape.
Bottom Line: The same things that are at risk in the enterprise today will still be at risk tomorrow – data, and business continuity. AI will pose new threats that the enterprise will have to cope with, and fighting AI with AI will become a necessity.
While short-term technology predictions can help drive resource planning, longer term prognostication can shape the evolution of your capabilities in using technology as a strategic asset. If you don’t have people with the right Agile expertise or automation know-how, it’s time to invest in those resources. If you aren’t exploring how to use predictive analytics and machine learning, then now is the time to begin. And it’s not too soon to start taking a hard look at your IT organization with an eye to retooling it for an age when technology is both highly distributed and decentralized.