By Tom Tovar, CEO of Appdome
Everyone knows the story of a frog placed in a pot of cold water. As the water heats up, the frog remains still until it’s too late. Today, the cyber function faces the same challenge as the frog, as the rest of the enterprise transitions to AI Native.
What is AI Native?
“AI Native” refers to organizations, teams, or functions that fully integrate artificial intelligence into core operations. Rather than treating AI as an add-on, these entities leverage AI as a foundational element of their business, execution, delivery, and decision-making. They operate with AI at their core, embedding it into every process for speed, automation, improved efficiency, and to reduce dependencies on human capital, and other resources.
The Enterprise-Wide Shift Towards AI Native
Across industries, enterprises are now shifting to an AI-Native approach. In 2025, key parts of the enterprise are moving beyond experimentation to complete restructuring. Departments, workflows, decision-making, and strategic planning are being reshaped around AI-driven automation and analysis for productivity. Key areas include:
- Software Development and Engineering: AI-powered coding assistants accelerate development, improve software quality, and streamline DevOps with automated testing and CI/CD processes.
- Marketing: AI-driven platforms analyze consumer behavior, enabling hyper-personalized campaigns and optimized ad spend.
- Customer Support & Experience: AI chatbots can handle customer service at scale, reducing dependence on humans.
- Fraud & Risk Management: AI enhances for fraud detection and risk modeling, quickly identifying anomalies and mitigating financial risks.
- Supply Chain and Logistics: AI predictive analytics optimize inventory while automating procurement and delivery.
- HR and Talent Management: AI streamlines recruitment, identifies top talent faster, and enhances workforce management.
The goal is clear: faster decision-making, increased efficiency, and minimized human error while maximizing value.
Cybersecurity Must Adapt…or Get Boiled Alive
Currently, cybersecurity teams focus on addressing the risks of AI adoption rather than embedding AI into their own cyber operations. This misalignment threatens their role as enterprises adopt AI-Native models at an accelerated pace. Without becoming AI-Native, the water will get too hot too fast. Cyber teams are falling behind as AI-Native organizations accelerate.
Why Cybersecurity Must Go AI-Native Now
Cybersecurity must go beyond AI-enhanced tools. Here are the top 5 reasons why the cyber teams need to go AI-Native:
- AI-Driven Threats Require AI-Driven Defense
Cybercriminals leapt into the AI boom to create highly sophisticated attacks, from deepfake-powered facial recognition bypasses to large-scale social engineering attacks at scale and autonomous malware evading detection. To counter these threats, organizations need an AI-Native defense that adapts, responds, and mitigates attacks in real time..
- Maintain Control of the Defense Lifecycle
An AI-Native approach automates the entire defense lifecycle, including defense delivery, compliance, threat identification, and incident response, as well as guiding end users through resolving an attack. Gone are the days when the cyber function and the security operation center (SOC) could rely on AI for threat detection, but still depend on manual processes to resolve threats. With AI-Native cybersecurity, teams can control automatically every aspect of defense, eliminating delays caused by dependencies on multiple departments and manual actions.
- Improve Decision-Making & Incident Response
Security leaders rely on multiple data sources, logs, and reports. AI-driven analytics provide deep insights and early warnings on emerging threats, along with benchmark comparisons and dynamic risk analysis. An AI-Native approach accelerates decision-making in incident response, automating defenses in real time before escalation.
- Eliminate Dependence on Other Departments
Many security teams are constrained by IT, engineering, and operations for critical tasks like threat modeling, infrastructure changes, and security tool integrations. With AI-Native defense, the cyber function can automate defense delivery independently of external teams. Now security teams can automate defense enforcement, reducing delays while accelerating security measures.
- Guarantee Business Protection and Revenue Security
As AI drives efficiency across enterprise functions, cybersecurity teams must keep up with rapid innovation. New applications, capabilities, revenue sources, threats, and vulnerabilities are evolving faster than ever. AI-Native security delivers continuous fraud prevention, automated security updates, and preemptive threat mitigation. With AI-Native, cyber and fraud defenses can be deployed instantly and ensure continuous defense.
Cyber’s Top Priority for 2025: Become AI Native.
Looking forward, CISOs and cybersecurity teams can no longer afford to see AI merely as a tool but must embrace AI as their foundation. Just as other enterprise functions use AI for speed, efficiency, and agility, cybersecurity must do the same – eliminating manual tasks, handoffs and learning curves.
With AI-Native, cyber teams use technology platforms to automate the entire defense lifecycle, ensuring readiness, reducing bottlenecks, and ensuring that security, ant-fraud and bot defense are delivered continuously. The future of cybersecurity isn’t just AI-aided — it’s AI-Native. Don’t be the cyber frog in the pot. The time to act is now.
Bridgetown Research raises $19M from Lightspeed and Accel to deploy AI business research agents
Posted in Commentary with tags Bridgetown Research on February 26, 2025 by itnerdStrategic business decisions have traditionally been expensive and slow for a fundamental reason: they don’t happen enough. This means companies lack both historical data to learn from and experts who have seen enough similar cases. Bridgetown Research is changing that. Today, the AI decision science startup announced $19 million in Series A funding led by Lightspeed and Accel, with participation from a leading research university.
Bridgetown Research has developed AI agents that autonomously execute research. Most notable amongst these agents are voice bots trained to recruit and interview industry experts, gathering primary data that can be analyzed alongside alternative data sourced from their partners.
Founded by Harsh Sahai, who previously led machine learning teams at Amazon before leading strategy engagements at McKinsey & Co., Bridgetown Research was born from a simple observation: the majority of business analyses are a permutation of a small number of automatable tasks. The founding team, comprising former professionals from McKinsey, Bain, Amazon, and leading tech startups, brings together extensive experience across strategy consulting and technology.
While many AI solutions focus on searching and summarizing information using LLMs, real world business decisions require much more than synthesising the open web. They need proprietary data such as primary data from experts and customer surveys, along with frameworks to understand markets, what Harsh Sahai calls “ontologies”. Moreover, outputs need to be repeatable and auditable for a business to use them to make decisions with tens of millions of dollars at stake. Bridgetown Research is the only player using agents to gather primary data and systematically find patterns in it to generate original insights.
Bridgetown Research started with a focus on private equity deal screening diligence. Multiple top-tier PE & VC firms already use Bridgetown Research for deal screening and deeper commercial diligence. They’re able to screen their pipeline much faster with initial analysis taking 24 hours instead of weeks without Bridgetown enabling teams to focus on actual decision making instead of research and analysis. For other customers Bridgetown has enabled voice of customer conversations that cover hundreds of respondents in parallel, and within days.
As global markets become increasingly complex, the demand for efficient and effective decision-making tools continues to rise. With this funding round, Bridgetown Research plans to invest further in training its AI agents to perform a broader set of analyses across a broader range of domains, and deepening industry partnerships to enhance access to domain-specific intelligence.
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